{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,12,29]],"date-time":"2024-12-29T00:40:15Z","timestamp":1735432815225,"version":"3.32.0"},"reference-count":105,"publisher":"MDPI AG","issue":"5","license":[{"start":{"date-parts":[[2018,5,10]],"date-time":"2018-05-10T00:00:00Z","timestamp":1525910400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Zhejiang Provincial Public Fund","award":["No. 2016C33136"]},{"name":"Spanish MINECO\/FEDER","award":["TRA2015-70501-C2-1-R"]},{"name":"Programas de actividades I+D (CAM)","award":["S2013\/MIT-2748"]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"Navigational assistance aims to help visually-impaired people to ambulate the environment safely and independently. This topic becomes challenging as it requires detecting a wide variety of scenes to provide higher level assistive awareness. Vision-based technologies with monocular detectors or depth sensors have sprung up within several years of research. These separate approaches have achieved remarkable results with relatively low processing time and have improved the mobility of impaired people to a large extent. However, running all detectors jointly increases the latency and burdens the computational resources. In this paper, we put forward seizing pixel-wise semantic segmentation to cover navigation-related perception needs in a unified way. This is critical not only for the terrain awareness regarding traversable areas, sidewalks, stairs and water hazards, but also for the avoidance of short-range obstacles, fast-approaching pedestrians and vehicles. The core of our unification proposal is a deep architecture, aimed at attaining efficient semantic understanding. We have integrated the approach in a wearable navigation system by incorporating robust depth segmentation. A comprehensive set of experiments prove the qualified accuracy over state-of-the-art methods while maintaining real-time speed. We also present a closed-loop field test involving real visually-impaired users, demonstrating the effectivity and versatility of the assistive framework.<\/jats:p>","DOI":"10.3390\/s18051506","type":"journal-article","created":{"date-parts":[[2018,5,11]],"date-time":"2018-05-11T07:42:48Z","timestamp":1526024568000},"page":"1506","source":"Crossref","is-referenced-by-count":80,"title":["Unifying Terrain Awareness for the Visually Impaired through Real-Time Semantic Segmentation"],"prefix":"10.3390","volume":"18","author":[{"given":"Kailun","family":"Yang","sequence":"first","affiliation":[{"name":"State Key Laboratory of Modern Optical Instrumentation, Zhejiang University, Hangzhou 310027, China"}]},{"given":"Kaiwei","family":"Wang","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Modern Optical Instrumentation, Zhejiang University, Hangzhou 310027, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0087-3077","authenticated-orcid":false,"given":"Luis M.","family":"Bergasa","sequence":"additional","affiliation":[{"name":"Department of Electronics, University of Alcal\u00e1, Madrid 28805, Spain"}]},{"given":"Eduardo","family":"Romera","sequence":"additional","affiliation":[{"name":"Department of Electronics, University of Alcal\u00e1, Madrid 28805, Spain"}]},{"given":"Weijian","family":"Hu","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Modern Optical Instrumentation, Zhejiang University, Hangzhou 310027, China"}]},{"given":"Dongming","family":"Sun","sequence":"additional","affiliation":[{"name":"Department of Computing, Imperial College London, London SW7 2AZ, UK"}]},{"given":"Junwei","family":"Sun","sequence":"additional","affiliation":[{"name":"KR-VISION Technology Co., Ltd., Hangzhou 310023, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7951-196X","authenticated-orcid":false,"given":"Ruiqi","family":"Cheng","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Modern Optical Instrumentation, Zhejiang University, Hangzhou 310027, China"}]},{"given":"Tianxue","family":"Chen","sequence":"additional","affiliation":[{"name":"Department of Electrical and Computer Engineering, University of California, Los Angeles, CA 90095, USA"}]},{"given":"Elena","family":"L\u00f3pez","sequence":"additional","affiliation":[{"name":"Department of Electronics, University of Alcal\u00e1, Madrid 28805, Spain"}]}],"member":"1968","published-online":{"date-parts":[[2018,5,10]]},"reference":[{"key":"ref_1","unstructured":"(2018, February 15). Terrain Awareness and Warning System. Available online: https:\/\/en.wikipedia.org\/wiki\/Terrain_awareness_and_warning_system."},{"key":"ref_2","unstructured":"Wang, S., and Yu, J. (2017). Everyday information behavior of the visually impaired in China. Inf. Res., 22, paper 743."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"149","DOI":"10.1007\/s12193-016-0235-6","article-title":"An insight into assistive technology for the visually impaired and blind people: State-of-the-art and future trends","volume":"11","author":"Bhowmick","year":"2017","journal-title":"J. Multimodal User Interfaces"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"e888","DOI":"10.1016\/S2214-109X(17)30293-0","article-title":"Magnitude, temporal trends, and projections of the global prevalence of blindness and distance and near vision impairment: A systematic review and meta-analysis","volume":"5","author":"Bourne","year":"2017","journal-title":"Lancet Glob. Health"},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Shao, L., Han, J., Kohli, P., and Zhang, Z. (2014). RGB-D sensor-based computer vision assistive technology for visually impaired persons. Computer Vision and Machine Learning with RGB-D Sensors, Springer.","DOI":"10.1007\/978-3-319-08651-4"},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Elmannai, W., and Elleithy, K. (2017). Sensor-based assistive devices for visually-impaired people: Current status, chanllenges, and future directions. Sensors, 17.","DOI":"10.3390\/s17030565"},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Pradeep, V., Medioni, G., and Weiland, J. (2010, January 13\u201318). Robot vision for the visually impaired. Proceedings of the 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), San Francisco, CA, USA.","DOI":"10.1109\/CVPRW.2010.5543579"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"25","DOI":"10.1109\/TSMCC.2009.2021255","article-title":"Wearable obstacle avoidance electronic travel aids for blind: A survey","volume":"40","author":"Dakopoulos","year":"2010","journal-title":"IEEE Trans. Syst. Man Cybern. Part C (Appl. Rev.)"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"17476","DOI":"10.3390\/s121217476","article-title":"Assisting the visually impaired: Obstacle detection and warning system by acoustic feedback","volume":"12","author":"Yebes","year":"2012","journal-title":"Sensors"},{"key":"ref_10","unstructured":"Rodr\u00edguez, A., Bergasa, L.M., Alcantarilla, P.F., Yebes, J., and Cela, A. (2012, January 3\u20137). Obstacle avoidance system for assisting visually impaired people. Proceedings of the IEEE Intelligent Vehicles Symposium Workshops, Madrid, Spain."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"617","DOI":"10.1007\/s12369-015-0313-z","article-title":"A walking assistant robotic system for the visually impaired based on computer vision and tactile perception","volume":"7","author":"Ni","year":"2015","journal-title":"Int. J. Soc. Robot."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Schwarze, T., Lauer, M., Schwaab, M., Romanovas, M., Bohm, S., and Jurgensohn, T. (2015, January 11\u201318). An intuitive mobility aid for visually impaired people based on stereo vision. Proceedings of the IEEE International Conference on Computer Vision Workshops, Santiago, Chile.","DOI":"10.1109\/ICCVW.2015.61"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"743","DOI":"10.3233\/AIS-170459","article-title":"IR stereo RealSense: Decreasing minimum range of navigational assistance for visualy impaired individuals","volume":"9","author":"Yang","year":"2017","journal-title":"J. Ambient Intell. Smart Environ."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"2809","DOI":"10.1364\/AO.57.002809","article-title":"Reducing the minimum range of a RGB-depth sensor to aid navigation in visually impaired individuals","volume":"57","author":"Yang","year":"2018","journal-title":"Appl. Opt."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Martinez, M., Roitberg, A., Koester, D., Stiefelhagen, R., and Schauerte, B. (2017, January 22\u201329). Using Technology Developed for Autonomous Cars to Help Navigate Blind People. Proceedings of the 2017 IEEE International Conference on Computer Vision Workshop (ICCVW), Venice, Italy.","DOI":"10.1109\/ICCVW.2017.169"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Caraiman, S., Morar, A., Owczarek, M., Burlacu, A., Rzeszotarski, D., Botezatu, N., Herghelegiu, P., Moldoveanu, F., Strumillo, P., and Moldoveanu, A. (2017, January 22\u201329). Computer Vision for the Visually Impaired: The Sound of Vision System. Proceedings of the IEEE Conference on Computer Vision Vision and Pattern Recognition, Venice, Italy.","DOI":"10.1109\/ICCVW.2017.175"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Herghelegiu, P., Burlacu, A., and Caraiman, S. (2017, January 19\u201321). Negative obstacle detection for wearable assistive devices for visually impaired. Proceedings of the 2017 21st International Conference on System Theory, Control and Computing (ICSTCC), Sinaia, Romania.","DOI":"10.1109\/ICSTCC.2017.8107095"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Koester, D., Schauerte, B., and Stiefelhagen, R. (2013, January 15\u201319). Accessible section detection for visual guidance. Proceedings of the 2013 IEEE International Conference on Multimedia and Expo Workshops, San Jose, CA, USA.","DOI":"10.1109\/ICMEW.2013.6618351"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Schauerte, B., Koester, D., Martinez, M., and Stiefelhagen, R. (2014, January 6\u201312). Way to go! Detecting open areas ahead of a walking person. Proceedings of the 2014 European Conference on Computer Vision, Zurich, Switzerland.","DOI":"10.1007\/978-3-319-16199-0_25"},{"key":"ref_20","unstructured":"Cheng, R., Wang, K., Yang, K., and Zhao, X. (2015, January 19). A ground and obstacle detection algorithm for the visually impaired. Proceedings of the IET International Conference on Biomedical Image and Signal Processing, Beijing, China."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Lin, Q., and Han, Y. (2016). A Dual-Field Sensing Scheme for a Guidance System for the Blind. Sensors, 16.","DOI":"10.3390\/s16050667"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Yang, K., Wang, K., Hu, W., and Bai, J. (2016). Expanding the detection of traversable area with RealSense for the visually impaired. Sensors, 16.","DOI":"10.3390\/s16111954"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"922","DOI":"10.1109\/JSYST.2014.2320639","article-title":"Navigation assistance for the visually impaired using RGB-D sensor with range expansion","volume":"10","author":"Aladren","year":"2016","journal-title":"IEEE Syst. J."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"3","DOI":"10.1016\/j.cviu.2016.03.019","article-title":"RGB-D camera based wearable navigation system for the visually impaired","volume":"149","author":"Lee","year":"2016","journal-title":"Comput. Vis. Image Underst."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Wang, H.C., Katzschmann, R.K., Teng, S., Araki, B., Giarr\u00e9, L., and Rus, D. (June, January 29). Enabling independent navigation for visually impaired people through a wearable vision-based feedback system. Proceedings of the 2017 IEEE International Conference on Robotics and Automation (ICRA), Singapore.","DOI":"10.1109\/ICRA.2017.7989772"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Saleh, K., Zeineldin, R.A., Hossny, M., Nahavandi, S., and El-Fishawy, N.A. (2017, January 5\u20138). Navigational Path Detection for the Visually Impaired using Fully Convolutional Networks. Proceedings of the IEEE Conference on Systems, Man and Cybernetics (SMC), Banff, AB, Canada.","DOI":"10.1109\/SMC.2017.8122809"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Perez-Yus, A., Bermudez-Cameo, J., Lopez-Nicolas, G., and Guerrero, J.J. (2017, January 22\u201329). Depth and Motion Cues with Phosphene Patterns for Prosthetic Vision. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Venice, Italy.","DOI":"10.1109\/ICCVW.2017.179"},{"key":"ref_28","unstructured":"Mehta, S., Hajishirzi, H., and Shapiro, L. (arXiv, 2017). Identifying Most Walkable Direction for Navigation in an Outdoor Environment, arXiv."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Burlacu, A., Baciu, A., Manta, V.I., and Caraiman, S. (2017, January 19\u201321). Ground geometry assessment in complex stereo vision based applications. Proceedings of the 2017 21st International Conference on System Theory, Control and Computing (ICSTCC), Sinaia, Romania.","DOI":"10.1109\/ICSTCC.2017.8107094"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"662","DOI":"10.1016\/j.procs.2016.05.356","article-title":"A new approach for automatic detection of tactile paving surfaces in sidewalks","volume":"80","author":"Ghilardi","year":"2016","journal-title":"Procedia Comput. Sci."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"186","DOI":"10.1016\/j.cviu.2016.01.011","article-title":"Pedestrian lane detection in unstructured scenes for assistive navigation","volume":"149","author":"Phung","year":"2016","journal-title":"Comput. Vis. Image Underst."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Ahmed, F., and Yeasin, M. (2017, January 14\u201319). Optimization and evaluation of deep architectures for ambient awareness on a sidewalk. Proceedings of the 2017 International Joint Conference on Neural Networks (IJCNN), Anchorage, AK, USA.","DOI":"10.1109\/IJCNN.2017.7966186"},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Li, Z., Rahman, M., Robucci, R., and Banerjee, N. (2017, January 12\u201314). PreSight: Enabling Real-Time Detection of Accessibility Problems on Sidewalks. Proceedings of the 2017 14th Annual IEEE International Conference on Sensing, Communication, and Networking (SECON), San Diego, CA, USA.","DOI":"10.1109\/SAHCN.2017.7964930"},{"key":"ref_34","unstructured":"Lee, Y.H., Leung, T.S., and Medioni, G. (2012, January 11\u201315). Real-time staircase detection from a wearable stereo system. Proceedings of the 2012 21st International Conference On Pattern Recognition (ICPR), Tsukuba, Japan."},{"key":"ref_35","unstructured":"Guerrero, J.J., P\u00e9rez-Yus, A., Guti\u00e9rrez-G\u00f3mez, D., Rituerto, A., and L\u00f3pez-Nicola\u00e1s, G. (2015, January 23\u201325). Human navigation assistance with a RGB-D sensor. Proceedings of the VI Congreso Internacional de Diseno, Redes de Investigacion y Tecnologia para todos (DRT4ALL), Madrid, Spain."},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Schwarze, T., and Zhong, Z. (2015, January 27\u201330). Stair detection and tracking from egocentric stereo vision. Proceedings of the 2015 IEEE International Conference on Image Processing (ICIP), Qu\u00e9bec City, QC, Canada.","DOI":"10.1109\/ICIP.2015.7351291"},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Munoz, R., Rong, X., and Tian, Y. (2016, January 11\u201315). Depth-aware indoor staircase detection and recognition for the visually impaired. Proceedings of the 2016 IEEE International Conference on Multimedia & Expo Workshops (ICMEW), Seattle, WA, USA.","DOI":"10.1109\/ICMEW.2016.7574706"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"192","DOI":"10.1016\/j.cviu.2016.04.007","article-title":"Stairs detection with odometry-aided traversal from a wearable RGB-D camera","volume":"154","author":"Guerrero","year":"2017","journal-title":"Comput. Vis. Image Underst."},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Stahlschmidt, C., von Camen, S., Gavriilidis, A., and Kummert, A. (July, January 28). Descending step classification using time-of-flight sensor data. Proceedings of the 2015 IEEE Intelligent Vehicles Symposium (IV), Seoul, Korea.","DOI":"10.1109\/IVS.2015.7225712"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"33","DOI":"10.1186\/s13640-016-0133-6","article-title":"Low-power depth-based descending stair detection for smart assistive devices","volume":"2016","author":"Cloix","year":"2016","journal-title":"EURASIP J. Image Video Process."},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Yang, K., Wang, K., Cheng, R., Hu, W., Huang, X., and Bai, J. (2017). Detecting traversable area and water hazards for the visually impaired with a pRGB-D sensor. Sensors, 17.","DOI":"10.3390\/s17081890"},{"key":"ref_42","unstructured":"(2018, February 15). KR-VISION Technology: To Tackle the Challenges for the Visually Impaired. Available online: http:\/\/krvision.cn\/."},{"key":"ref_43","unstructured":"Yang, K., Wang, K., Cheng, R., and Zhu, X. (2015, January 19). A new approach of point cloud processing and scene segmentation for guiding the visually impaired. Proceedings of the IET International Conference on Biomedical Image and Signal Processing, Beijing, China."},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Mocanu, B., Tapu, R., and Zaharia, T. (2016). When ultrasonic sensors and computer vision join forces for efficient obstacle detection and recognition. Sensors, 16.","DOI":"10.3390\/s16111807"},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Rizzo, J.R., Pan, Y., Hudson, T., Wong, E.K., and Fang, Y. (2017, January 4\u20136). Sensor fusion for ecologically valid obstacle identification: Building a comprehensive assistive technology platform for the visually impaired. Proceedings of the 2017 7th International Conference on Modeling, Simulation, and Applied Optimization (ICMSAO), Sharjah, UAE.","DOI":"10.1109\/ICMSAO.2017.7934891"},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"258","DOI":"10.1109\/TCE.2017.014980","article-title":"Smart guiding glasses for visually impaired people in indoor environment","volume":"63","author":"Bai","year":"2017","journal-title":"IEEE Trans. Consum. Electron."},{"key":"ref_47","unstructured":"Pisa, S., Piuzzi, E., Pittella, E., and Affronti, G. (September, January 30). A FMCW radar as electronic travel aid for visually impaired subjects. Proceedings of the XXI IMEKO World Congress \u201cMeasurement in Research and Industry\u201d, Prague, Czech Republic."},{"key":"ref_48","doi-asserted-by":"crossref","unstructured":"Di Mattia, V., Petrini, V., Pieralisi, M., Manfredi, G., De Leo, A., Russo, P., Cerri, G., and Scalise, L. (December, January 30). A K-band miniaturized antenna for safe mobility of visually impaired people. Proceedings of the 2015 IEEE 15th Mediterranean Microwave Symposium (MMS), Lecce, Italy.","DOI":"10.1109\/MMS.2015.7375449"},{"key":"ref_49","doi-asserted-by":"crossref","unstructured":"Di Mattia, V., Manfredi, G., De Leo, A., Russo, P., Scalise, L., Cerri, G., and Scalise, L. (2016, January 7\u20139). A feasibility study of a compact radar system for autonomous walking of blind people. Proceedings of the 2016 IEEE 2nd International Forum on Research and Technologies for Society and Industry Leveraging a Better Tomorrow (RTSI), Bologna, Italy.","DOI":"10.1109\/RTSI.2016.7740599"},{"key":"ref_50","doi-asserted-by":"crossref","unstructured":"Kwiatkowski, P., Jaeschke, T., Starke, D., Piotrowsky, L., Deis, H., and Pohl, N. (2017, January 15\u201317). A concept study for a radar-based navigation device with sector scan antenna for visually impaired people. Proceedings of the 2017 First IEEE MTT-S International Microwave Bio Conference (IMBIOC), Gothenburg, Sweden.","DOI":"10.1109\/IMBIOC.2017.7965796"},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"436","DOI":"10.1038\/nature14539","article-title":"Deep learning","volume":"521","author":"LeCun","year":"2015","journal-title":"Nature"},{"key":"ref_52","unstructured":"Krizhevsky, A., Sutskever, I., and Hinton, G.E. (2012, January 3\u20138). Imagenet classification with deep convolutional neural networks. Proceedings of the Advances in neural information processing systems, Lake Tahoe, NV, USA."},{"key":"ref_53","unstructured":"Ren, S., He, K., Girshick, R., and Sun, J. (2015, January 7\u201312). Faster r-cnn: Towards real-time object detection with region proposal networks. Proceedings of the Advances in Neural Information Processing Systems, Montr\u00e9al, QC, Canada."},{"key":"ref_54","doi-asserted-by":"crossref","unstructured":"Long, J., Shelhamer, E., and Darrel, T. (2015, January 8\u201310). Fully convolutional networks for semantic segmentation. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Boston, MA, USA.","DOI":"10.1109\/CVPR.2015.7298965"},{"key":"ref_55","doi-asserted-by":"crossref","unstructured":"He, K., Gkioxari, G., Doll\u00e1r, P., and Girshick, R. (2017, January 22\u201329). Mask R-CNN. Proceedings of the 2017 IEEE International Conference on Computer Vision (ICVV), Venice, Italy.","DOI":"10.1109\/ICCV.2017.322"},{"key":"ref_56","unstructured":"Romera, E., Bergasa, L.M., and Arroyo, R. (arXiv, 2016). Can we unify monocular detectors for autonomous driving by using the pixel-wise semantic segmentation of CNNS?, arXiv."},{"key":"ref_57","doi-asserted-by":"crossref","unstructured":"Zhou, B., Zhao, H., Puig, X., Fidler, S., Barriuso, A., and Torralba, A. (arXiv, 2016). Semantic understanding of scenes through the ADE20K dataset, arXiv.","DOI":"10.1109\/CVPR.2017.544"},{"key":"ref_58","doi-asserted-by":"crossref","unstructured":"Mottaghi, R., Chen, X., Liu, X., Cho, N.G., Lee, S.W., Fidler, S., Urtasun, R., and Yuille, A. (2014, January 24\u201327). The role of context for object detection and semantic segmentation in the wild. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Columbus, OH, USA.","DOI":"10.1109\/CVPR.2014.119"},{"key":"ref_59","unstructured":"Caesar, H., Uijlings, J., and Ferrari, V. (arXiv, 2016). COCO-Stuff: Thing and stuff classes in context, arXiv."},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"2481","DOI":"10.1109\/TPAMI.2016.2644615","article-title":"Segnet: A deep convolutional encoder-decoder architecture for image segmentation","volume":"39","author":"Badrinarayanan","year":"2017","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_61","unstructured":"Paszke, A., Chaurasia, A., Kim, S., and Culurciello, E. (arXiv, 2016). Enet: A deep neural network architecture for real-time semantic segmentation, arXiv."},{"key":"ref_62","doi-asserted-by":"crossref","unstructured":"Ronneberger, O., Fischer, P., and Brox, T. (2015, January 5\u20139). U-net: Convolutional networks for biomedical image segmentation. Proceedings of the International Conference on Medical Image Computing and Computer-Assisted Intervention, Munich, Germany.","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"ref_63","unstructured":"Treml, M., Arjona-Medina, J., Unterthiner, T., Durgesh, R., Friedmann, F., Schuberth, P., Mayr, A., Heusel, M., Hofmarcher, M., and Widrich, M. (2016, January 5\u201310). Speeding up semantic segmentation for autonomous driving. Proceedings of the MLLITS, NIPS Workshop, Barcelona, Spain."},{"key":"ref_64","doi-asserted-by":"crossref","unstructured":"Romera, E., Alvarez, J.M., Bergasa, L.M., and Arroyo, R. (2017, January 11\u201314). Efficient convnet for real-time semantic segmentation. Proceedings of the 2017 IEEE Intelligent Vehicles Symposium (IV), Redondo Beach, CV, USA.","DOI":"10.1109\/IVS.2017.7995966"},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"263","DOI":"10.1109\/TITS.2017.2750080","article-title":"Arroyo, R. ERFNet: Efficient Residual Factorized ConvNet for Real-Time Semantic Segmentation","volume":"19","author":"Romera","year":"2018","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"ref_66","doi-asserted-by":"crossref","unstructured":"Zhao, H., Qi, X., Shen, X., Shi, J., and Jia, J. (arXiv, 2017). Icnet for real-time semantic segmentation on high-resolution images, arXiv.","DOI":"10.1007\/978-3-030-01219-9_25"},{"key":"ref_67","doi-asserted-by":"crossref","unstructured":"Chaurasia, A., and Culurciello, E. (arXiv, 2017). LinkNet: Exploiting Encoder Representations for Efficient Semantic Segmentation, arXiv.","DOI":"10.1109\/VCIP.2017.8305148"},{"key":"ref_68","unstructured":"Oliveira, G.L., Bollen, C., Burgard, W., and Brox, T. (2017). Efficient and robust deep networks for semantic segmentation. Int. J. Robot. Res., 0278364917710542."},{"key":"ref_69","doi-asserted-by":"crossref","unstructured":"Horne, L., Alvarez, J.M., McCarthy, C., and Barnes, N. (2015, January 25\u201329). Semantic labelling to aid navigation in prosthetic vision. Proceedings of the 2015 37th Annual Internal Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Milan, Italy.","DOI":"10.1109\/EMBC.2015.7319117"},{"key":"ref_70","doi-asserted-by":"crossref","first-page":"113","DOI":"10.1016\/j.cviu.2016.02.015","article-title":"Semantic labeling for prosthetic vision","volume":"149","author":"Horne","year":"2016","journal-title":"Comput. Vis. Image Underst."},{"key":"ref_71","unstructured":"(2018, February 15). Terrain Awareness Dataset. Available online: http:\/\/wangkaiwei.org\/projecteg.html."},{"key":"ref_72","doi-asserted-by":"crossref","unstructured":"Fischler, M.A., and Bolles, R.C. (1987). Random sample consensus: A paradigm for model fitting with applications to image analysis and automated cartography. Readings in Computer Vision, Elsevier.","DOI":"10.1016\/B978-0-08-051581-6.50070-2"},{"key":"ref_73","doi-asserted-by":"crossref","unstructured":"Leong, K.Y., Egerton, S., and Chan, C.K. (2017, January 13\u201315). A wearable technology to negotiate surface discontinuities for the blind and low vision. Proceedings of the 2017 IEEE Life Sciences Conference (LSC), Sydney, Australia.","DOI":"10.1109\/LSC.2017.8268157"},{"key":"ref_74","doi-asserted-by":"crossref","unstructured":"Wedel, A., Franke, U., Badino, H., and Cremers, D. (2008, January 4\u20136). B-spline modeling of road surfaces for freespace estimation. Proceedings of the 2008 IEEE Intelligent Vehicles Symposium, Eindhoven, The Netherlands.","DOI":"10.1109\/IVS.2008.4621254"},{"key":"ref_75","doi-asserted-by":"crossref","unstructured":"Badino, H., Franke, U., and Pfeiffer, D. (2009, January 9\u201311). The stixel world-a compact medium level representation of the 3D-world. Proceedings of the Joint Pattern Recognition Symposium, Jena, Germany.","DOI":"10.1007\/978-3-642-03798-6_6"},{"key":"ref_76","doi-asserted-by":"crossref","unstructured":"Furukawa, Y., Curless, B., Seitz, S.M., and Szeliski, R. (2009, January 20\u201325). Manhattan-world stereo. Proceedings of the IEEE Computer Vision and Pattern Recognition, Miami, FL, USA.","DOI":"10.1109\/CVPRW.2009.5206867"},{"key":"ref_77","unstructured":"Geiger, A., Roser, M., and Urtasun, R. (2010, January 8\u201312). Efficient large-scale stereo matching. Proceedings of the Asian Conference on Computer Vision, Queenstown, New Zealand."},{"key":"ref_78","doi-asserted-by":"crossref","unstructured":"He, K., Sun, J., and Tang, X. (2010, January 5\u201311). Guided image filtering. Proceedings of the European Conference on Computer Vision, Crete, Greece.","DOI":"10.1007\/978-3-642-15549-9_1"},{"key":"ref_79","doi-asserted-by":"crossref","unstructured":"Poggi, M., Nanni, L., and Mattoccia, S. (2015, January 7\u201311). Crosswalk recognition through point-cloud processing and deep-learning suited to a wearable mobility aid for the visually impaired. Proceedings of the International Conference on Image Analysis and Processing, Genova, Italy.","DOI":"10.1007\/978-3-319-23222-5_35"},{"key":"ref_80","doi-asserted-by":"crossref","first-page":"053025","DOI":"10.1117\/1.JEI.26.5.053025","article-title":"Crosswalk navigation for people with visual impairments on a wearable device","volume":"26","author":"Cheng","year":"2017","journal-title":"J. Electron. Imaging"},{"key":"ref_81","doi-asserted-by":"crossref","unstructured":"Cheng, R., Wang, K., Yang, K., Long, N., Bai, J., and Liu, D. (2017). Real-time pedestrian crossing lights detection algorithm for the visually impaired. Multimedia Tools Appl., 1\u201321.","DOI":"10.1007\/s11042-017-5472-5"},{"key":"ref_82","doi-asserted-by":"crossref","unstructured":"Miksik, O., Vineet, V., Lidegaard, M., Prasaath, R., Nie\u00dfner, M., Golodetz, S., Hicks, S.L., Perez, P., Izadi, S., and Torr, P.H.S. (2015, January 18\u201323). The semantic paintbrush: Interactive 3D mapping and recognition in large outdoor spaces. Proceedings of the 33rd Annual ACM Conference on Human Factors in Computing Systems, Seoul, Korea.","DOI":"10.1145\/2702123.2702222"},{"key":"ref_83","unstructured":"He, K., Zhang, X., Ren, S., and Sun, J. (July, January 26). Deep residual learning for image recognition. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA."},{"key":"ref_84","doi-asserted-by":"crossref","unstructured":"Zhao, H., Shi, J., Qi, X., Wang, X., and Jia, J. (2017, January 21\u201326). Pyramid scene parsing network. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI.","DOI":"10.1109\/CVPR.2017.660"},{"key":"ref_85","doi-asserted-by":"crossref","unstructured":"Keselman, L., Woodfill, J.I., Grunnet-Jepsen, A., and Bhowmik, A. (arXiv, 2017). Intel RealSense Stereoscopic Depth Cameras, arXiv.","DOI":"10.1109\/CVPRW.2017.167"},{"key":"ref_86","unstructured":"(2018, February 15). AfterShokz: Bone Conduction Headphones. Available online: https:\/\/aftershokz.com\/."},{"key":"ref_87","doi-asserted-by":"crossref","unstructured":"Konolige, K. (2010, January 3\u20138). Projected texture stereo. Proceedings of the 2010 IEEE International Conference on Robotics and Automation (ICRA), Anchorage, AK, USA.","DOI":"10.1109\/ROBOT.2010.5509796"},{"key":"ref_88","doi-asserted-by":"crossref","first-page":"328","DOI":"10.1109\/TPAMI.2007.1166","article-title":"Stereo processing by semiglobal matching and mutual information","volume":"30","author":"Hirschmuller","year":"2008","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_89","doi-asserted-by":"crossref","unstructured":"Zagoruyko, S., and Komodakis, N. (arXiv, 2016). Wide residual networks, arXiv.","DOI":"10.5244\/C.30.87"},{"key":"ref_90","unstructured":"Alvarez, L., and Petersson, L. (arXiv, 2016). Decomposeme: Simplifying convnets for end-to-end learning, arXiv."},{"key":"ref_91","doi-asserted-by":"crossref","unstructured":"Jaderberg, M., Vedaldi, A., and Zisserman, A. (arXiv, 2014). Speeding up convolutional neural networks with low rank expansions, arXiv.","DOI":"10.5244\/C.28.88"},{"key":"ref_92","doi-asserted-by":"crossref","unstructured":"Rigamonti, R., Sironi, A., Lepetit, V., and Fua, P. (2013, January 23\u201328). Learning separable filters. Proceedings of the 2013 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Portland, OR, USA.","DOI":"10.1109\/CVPR.2013.355"},{"key":"ref_93","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., and Sun, J. (2015, January 7\u201312). Delving deep into rectifiers: Surpassing human-level performance on imagenet classification. Proceedings of the IEEE International Conference on Computer Vision, Boston, MA, USA.","DOI":"10.1109\/ICCV.2015.123"},{"key":"ref_94","unstructured":"Kingma, D.P., and Ba, J. (arXiv, 2014). Adam: A method for stochastic optimization, arXiv."},{"key":"ref_95","doi-asserted-by":"crossref","first-page":"211","DOI":"10.1007\/s11263-015-0816-y","article-title":"Imagenet large scale visual recognition challenge","volume":"115","author":"Russakovsky","year":"2015","journal-title":"J. Comput. Vis."},{"key":"ref_96","unstructured":"Ioffe, S., and Szegedy, C. (2015, January 6\u201311). Batch normalization: Accelerating deep network training by reducing internal covariate shift. Proceedings of the International conference on machine learning, Lille, France."},{"key":"ref_97","unstructured":"Hinton, G.E., Srivastava, N., Krizhevsky, A., Sutskever, I., and Salakhutdinov, R.R. (arXiv, 2012). Improving neural networks by preventing co-adaptation of feature detectors, arXiv."},{"key":"ref_98","unstructured":"S\u00fcnderhauf, N., Shirazi, S., Dayoub, F., Upcroft, B., and Milford, M. (October, January 28). On the performance of convnet features for place recognition. Proceedings of the IEEE\/RSJ International Conference on Intelligent Robots and Systems (IROS), Hamburg, Germany."},{"key":"ref_99","doi-asserted-by":"crossref","first-page":"623","DOI":"10.1007\/s00221-012-3340-0","article-title":"Evidence for enhanced discrimination of virtual auditory distance among blind listeners using level and direct-to-reverberant cues","volume":"224","author":"Kolarik","year":"2013","journal-title":"Exp. Brain Res."},{"key":"ref_100","doi-asserted-by":"crossref","first-page":"515","DOI":"10.1007\/s00221-005-2329-3","article-title":"Enhanced sensitivity to echo cues in blind subjects","volume":"165","author":"Dufour","year":"2005","journal-title":"Exp. Brain Res."},{"key":"ref_101","unstructured":"Grond, F., and Berger, J. (2011). Parameter mapping sonification. The Sonification Handbook, Logos Verlag Berlin GmbH."},{"key":"ref_102","unstructured":"(2018, February 15). Shepard Tone. Available online: https:\/\/en.wikipedia.org\/wiki\/Shepard_tone."},{"key":"ref_103","unstructured":"(2018, February 15). FMOD. Available online: https:\/\/www.fmod.com."},{"key":"ref_104","unstructured":"(2018, February 15). AMAP. Available online: http:\/\/www.autonavi.com\/."},{"key":"ref_105","doi-asserted-by":"crossref","unstructured":"Li, W.H. (2013, January 1\u20138). Wearable computer vision systems for a cortical visual prosthesis. Proceedings of the 2013 IEEE International Conference on Computer Vision Workshops (ICCVW), Sydney, Australia.","DOI":"10.1109\/ICCVW.2013.63"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/18\/5\/1506\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,12,29]],"date-time":"2024-12-29T00:02:09Z","timestamp":1735430529000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/18\/5\/1506"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2018,5,10]]},"references-count":105,"journal-issue":{"issue":"5","published-online":{"date-parts":[[2018,5]]}},"alternative-id":["s18051506"],"URL":"https:\/\/doi.org\/10.3390\/s18051506","relation":{},"ISSN":["1424-8220"],"issn-type":[{"type":"electronic","value":"1424-8220"}],"subject":[],"published":{"date-parts":[[2018,5,10]]}}}