Abstract
Because the current troubleshooting of various abnormal behaviors in surveillance video requires a lot of manpower and can’t be processed in time, this paper proposes abnormal behaviors analysis in a specific scenario based on SSD, so that the surveillance camera can detect and recognize the abnormal behavior of the object in real time. The algorithm in this paper is applied to the surveillance video scene of the general hotel front desk. The SSD backbone network is used to extract the convolution feature and the average pool feature of the dataset, and then multi-scale classification of feature maps of certain feature layers and regression, and finally through the NMS processing output algorithm finally detected the object’s confidence and coordinate. Experiments show that the algorithm of our proposed algorithm is close to 90%, and the processing speed reaches 15FPS, which basically meets the real-time detection of abnormal human behaviors in specific scenes in surveillance video.
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Acknowledgments
This research is supported by the 2018KJY0203 technology project of Chengdu University of Technology in 2018.
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He, J., Ma, C., Tan, X., Huang, Y., Sun, J., He, L. (2020). Analysis of Abnormal Behaviors in Specific Scenarios Based on SSD. In: Hung, J., Yen, N., Chang, JW. (eds) Frontier Computing. FC 2019. Lecture Notes in Electrical Engineering, vol 551. Springer, Singapore. https://doi.org/10.1007/978-981-15-3250-4_184
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DOI: https://doi.org/10.1007/978-981-15-3250-4_184
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