Abstract
Crowd behavior recognition under complex surveillance scenarios is a fundamental and important problem in crowd management application. In this paper, a comprehensive and specific overall-level dynamic attribute package is proposed by considering local pattern-related motion and group-level motion together to represent crowd movement. Curl and divergence map of normalized average motion vector field act as local pattern-related motion, which represents physical movement tendency of each particle. Group-level motion explores crowd interaction of inter-/intra-group, which focus on depicting crowd’s social dynamic property. The complementary characteristic of two motion representation in different level is analyzed and verified. Single frames in video clips and the corresponding dynamic attribute packages are sent into two-branch structured ConvNet, which can extract more discriminative spatial-temporal feature for behavior recognition. Experiment results conducted on CUHK dataset show that the proposed crowd behavior recognition framework outperforms than existing approaches and obtains the state-of-art performance.
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Acknowledgement
This work was supported in part by National Natural Science Foundation of China (NSFC, Grant No. 61771303 and 61671289), Science and Technology Commission of Shanghai Municipality (STCSM, Grant Nos. 17DZ1205602, 18DZ1200-102, 18DZ2270700), and SJTUYitu/Thinkforce Joint laboratory for visual computing and application. Director Fund of PSRPC.
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Shi, T., Yang, H., Chen, L., Zhu, J. (2019). Dynamic Attribute Package: Crowd Behavior Recognition in Complex Scene. In: Zhai, G., Zhou, J., An, P., Yang, X. (eds) Digital TV and Multimedia Communication. IFTC 2018. Communications in Computer and Information Science, vol 1009. Springer, Singapore. https://doi.org/10.1007/978-981-13-8138-6_36
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DOI: https://doi.org/10.1007/978-981-13-8138-6_36
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