Abstract
In this paper, we propose a simple nonlinear filter which improves the detection of pedestrians walking in a video. We do so by first cumulating temporal gradient of moving objects into a motion history image (MHI). Then we apply to each frame of the video a motion-guided nonlinear filter whose goal is to smudge out background details while leaving untouched foreground moving objects. The resulting blurry-background image is then fed to a pedestrian detector. Experiments reveal that for a given miss rate, our motion-guided nonlinear filter can decrease the number of false positives per image (FPPI) by a factor of up to 26. Our method is simple, computationally light, and can be applied on a variety of videos to improve the performances of almost any kind of pedestrian detectors.
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Benenson, R., Mathias, M., Timofte, R., Van Gool, L.: Pedestrian detection at 100 frames per second. In: IEEE-C-CVPR, pp. 2903–2910 (2012)
Chen, G., Ding, Y., Xiao, J., Han, T.X.: Detection evolution with multi-order contextual co-occurrence. In: IEEE-C-CVPR, pp. 1798–1805 (2013)
Dalal, N., Triggs, B., Schmid, C.: Human detection using oriented histograms of flow and appearance. In: Leonardis, A., Bischof, H., Pinz, A. (eds.) ECCV 2006. LNCS, vol. 3952, pp. 428–441. Springer, Heidelberg (2006)
Davis, J.W.: Hierarchical motion history images for recognizing human motion. In: IEEE-W-Det. and Rec. of Eve. in Vid., pp. 39–46 (2001)
Dollar, P., Wojek, C., Schiele, B., Perona, P.: Pedestrian detection: An evaluation of the state of the art. IEEE-T-PAMI 34(4), 743–761 (2012)
Felzenszwalb, P.F., Girshick, R.B., McAllester, D., Ramanan, D.: Object detection with discriminatively trained part based models. IEEE-T-PAMI (2010)
Fernández-Caballero, A., Castillo, J., Serrano-Cuerda, J., Maldonado-Bascón, S.: Real-time human segmentation in infrared videos. Exp. Sys. with App. 38(3), 2577–2584 (2011)
Lin, Z., Davis, L.S.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE-T-PAMI 32(4), 604–618 (2010)
Ouyang, W., Wang, X.: Single-pedestrian detection aided by multi-pedestrian detection. In: IEEE-C-CVPR, pp. 3198–3205 (2013)
Sermanet, P., Kavukcuoglu, K., Chintala, S., LeCun, Y.: Pedestrian detection with unsupervised multi-stage feature learning. In: IEEE-C-CVPR (2013)
Wang, M., Li, W., Wang, X.: Transferring a generic pedestrian detector towards specific scenes. In: IEEE-C-CVPR, pp. 3274–3281 (2012)
Wang, X., Wang, M., Li, W.: Scene-specific pedestrian detection for static video surveillance. IEEE-T-PAMI 36(2), 361–374 (2014)
Wang, Y., Jodoin, P.M., Porikli, F., Konrad, J., Benezeth, Y., Ishwar, P.: Cdnet 2014: an expanded change detection benchmark dataset. In: IEEE-W-CVPRW, pp. 393–400 (2014)
Wojek, C., Schiele, B.: A performance evaluation of single and multi-feature people detection. In: Pat. Rec., pp. 82–91 (2008)
Wu, J., Rehg, J.M.: Centrist: A visual descriptor for scene categorization. IEEE-T-PAMI 33(8), 1489–1501 (2011)
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Wang, Y., Piérard, S., Su, SZ., Jodoin, PM. (2015). Nonlinear Background Filter to Improve Pedestrian Detection. In: Murino, V., Puppo, E., Sona, D., Cristani, M., Sansone, C. (eds) New Trends in Image Analysis and Processing -- ICIAP 2015 Workshops. ICIAP 2015. Lecture Notes in Computer Science(), vol 9281. Springer, Cham. https://doi.org/10.1007/978-3-319-23222-5_65
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DOI: https://doi.org/10.1007/978-3-319-23222-5_65
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