Abstract
Aiming at the fixed-view video surveillance scene, this paper proposes a video object detection method that combines motion features and YOLO. The method uses the method of filtering video frames without motion features and segmenting video frames with motion features to reduce the reasoning pressure of the YOLO algorithm model. In this process, video frames containing moving objects are first obtained by the moving object detection module. Second, the moving target will be recognized by the object of interest recognition module. Finally, the background decision module records and analyzes the detection results to obtain background model updates or result output. It detects moving objects without using traditional background modeling methods. Experiments based on the CDnet2014 dataset show that our method improves the missed detection rate by 0.098% and the average inference speed per frame by 45.62% compared with the YOLO-based humanoid detection method. Furthermore, the method has superior performance in scenarios where target objects appear less frequently (substations, transmission lines, and hazardous areas).
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References
Shifa, A., et al.: MuLViS: multi-level encryption based security system for surveillance videos. IEEE Access 8, 177131–177155 (2020). https://doi.org/10.1109/ACCESS.2020.3024926
Hou, H., Li, N., Liu, D.: Research on moving object detection in intelligent video surveillance. Comput. Technol. Dev. 22(2), 49–52 (2012)
Wan, Y., Han, Y., Lu, H.: The methods for moving object detection. Comput. Simul. 23(10), 221–226 (2006)
Lipton, A.J., Fujiyoshi, H., Patil, R.S.: Moving target classification and tracking from real-time video. In: Proceedings Fourth IEEE Workshop on Applications of Computer Vision, WACV 1998 (Cat. No. 98EX201), pp. 8–14 (1998). https://doi.org/10.1109/ACV.1998.732851
Alzughaibi, A.D., Hakami, H.A., Chaczko, Z.: Review of human motion detection based on background subtraction techniques. Int. J. Comput. Appl. 122, 1–5 (2015)
Wu, L., Liu, Z., Li, Y.: Moving objects detection based on embedded video surveillance. In: 2012 International Conference on Systems and Informatics (ICSAI 2012), pp. 2042–2045 (2012). https://doi.org/10.1109/IC-SAI.2012.6223453
Lijing, C., Ke, T., Zhonglin, F.: Moving target recognition under static background. In: 2014 Fourth International Conference on Instrumentation and Measurement, Computer, Communication and Control, pp. 117–120 (2014). https://doi.org/10.1109/IMCCC.2014.32
Redmon, J., Divvala, S., Girshick, R., Farhadi, A.: You Only Look Once: Unified, Real-Time Object Detection, pp. 779–788 (2016)
Liu, W., Anguelov, D., Erhan, D., Szegedy, C., Reed, S., Fu, C.-Y., Berg, A.C.: SSD: single shot multibox detector. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9905, pp. 21–37. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-46448-0_2
Boyuan, W., Muqing, W.: Study on pedestrian detection based on an improved YOLOv4 algorithm. In: 2020 IEEE 6th International Conference on Computer and Communications (ICCC), pp. 1198–1202 (2020). https://doi.org/10.1109/ICCC51575.2020.9344983
Fei, R., Ou, L.: A video object detector based on deep reinforcement learning and correction module. In: 2020 IEEE 3rd International Conference on Electronics and Communication Engineering (ICECE), pp. 1–5 (2020). https://doi.org/10.1109/ICECE51594.2020.9353034
Peng, Q., et al.: Pedestrian detection for transformer substation based on Gaussian mixture model and YOLO. In: 2016 8th International Conference on Intelligent Human–Machine Systems and Cybernetics (IHMSC), pp. 562–565 (2016). https://doi.org/10.1109/IHMSC.2016.130
Lu, Y., Chen, Y., Zhao, D., Li, H.: Hybrid deep learning based moving object detection via motion prediction. In: 2018 Chinese Automation Congress (CAC), pp. 1442–1447 (2018). https://doi.org/10.1109/CAC.2018.8623038
Everingham, M., Van Gool, L., Williams, C.K.I., Winn, J., Zisserman, A.: The pascal visual object classes (VOC) challenge. Int. J. Comput. Vis. 88(2), 303–338 (2010)
Goyette, N., Jodoin, P., Porikli, F., Konrad, J., Ishwar, P.: Changedetection.net: a new change detection benchmark dataset. In: 2012 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, pp. 1–8 (2012). https://doi.org/10.1109/CVPRW.2012.6238919
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Xiao, C. et al. (2022). Research on Video Object Detection Methods Based on YOLO with Motion Features. In: Wang, Y., Zhu, G., Han, Q., Wang, H., Song, X., Lu, Z. (eds) Data Science. ICPCSEE 2022. Communications in Computer and Information Science, vol 1628. Springer, Singapore. https://doi.org/10.1007/978-981-19-5194-7_27
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DOI: https://doi.org/10.1007/978-981-19-5194-7_27
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