Abstract
Video surveillance based crowd counting is important for crowd management and public security. It is a challenge task due to the cluttered background, ambiguous foreground and diverse crowd distributions. In this paper, we propose an end-to-end crowd counting method with convolutional neural networks, which integrates original frames and motion cues for learning a deep crowd counting regressor. The original frames and motion cues are complementary to each other for counting the stationary and moving pedestrians. Experimental results on two widely-used crowd counting datasets demonstrate the effectiveness of our method, and achieve the state-of-the-art performance.
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Acknowledgment
This work is funded by the National Natural Science Foundation of China (Grant No. 61602433 and Grant No. 61472386), and the Strategic Priority Research Program of the Chinese Academy of Sciences (Grant XDA06040103). The two Titan X GPUs used for this research were donated by the NVIDIA Corporation.
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Zhang, D., Li, Z., Liu, P. (2016). Surveillance Based Crowd Counting via Convolutional Neural Networks. In: Zhang, Z., Huang, K. (eds) Intelligent Visual Surveillance. IVS 2016. Communications in Computer and Information Science, vol 664. Springer, Singapore. https://doi.org/10.1007/978-981-10-3476-3_17
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DOI: https://doi.org/10.1007/978-981-10-3476-3_17
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