Abstract
Vehicle detection is a fundamental challenge in urban traffic surveillance video. Due to the powerful representation ability of convolution neural network (CNN), CNN-based detection approaches have achieve incredible success on generic object detection. However, they can’t deal well with vehicle occlusion in complex urban traffic scene. In this paper, we present a new occlusion-aware vehicle detection CNN framework, which is an effective and efficient framework for vehicle detection. First, we concatenate the low-level and high-level feature maps to capture more robust feature representation, then we fuse the local and global feature maps for handling vehicle occlusion, the context information is also been adopted in our framework. Extensive experiments demonstrate the competitive performance of our proposed framework. Our methods achieve better effect than primal Faster R-CNN in terms of accuracy on a new urban traffic surveillance dataset (UTSD) which contains a mass of occlusion vehicles and complex scenes.
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Acknowledgments
This research was supported by the National Natural Science Foundation of China (Grant No. 61806088). Natural Science Fund of Changzhou (CE20175026), Qing Lan Project of Jiangsu Province. The Science and Technology Support Plan of Changzhou (Social Development, CE20185044). The Science and Technology Achievements Transformation Project of Nanjing Association for Science and Technology (201701209).
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Chen, L., Ruan, Y., Fan, H. et al. Occlusion-Aware Detection for Internet of Vehicles in Urban Traffic Sensing Systems. Mobile Netw Appl 26, 981–987 (2021). https://doi.org/10.1007/s11036-020-01668-3
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DOI: https://doi.org/10.1007/s11036-020-01668-3