To ensure the public’s safety such as in buses, it is very important to accurately judge people’s behaviors and give early warnings. If by watching the video surveillance manually, the cost will be very high, and it cannot be effectively popularized, so video automatic monitoring is preferred. For buses, its environmental space is closed as well as narrow, and at the same time, it is often in a non-stationary state, so traditional behavior detection methods cannot be used here as they are easily affected by moving environment and difficult to fulfill object behavior identification in real time. Aiming at this problem, for people’s fast-moving in buses, a kind of detection method based on YOLOv5 is proposed in this paper. Firstly, the method detects people through one-stage object detection. Secondly, in order to obtain the person's movement trajectory quickly and accurately, an improved two-stage object matching algorithm is designed to track different people. Then, the speed curves of a person during normal activities and fast moving are compared. Finally, an abnormal alarm mechanism is constructed to realize the effective fast movement alarm. Surveillance video in the bus was used to test and evaluate the effectiveness of the method. Results show that the accuracy rate of our method can get 95.4%.
| Published in | International Journal of Sensors and Sensor Networks (Volume 9, Issue 1) |
| DOI | 10.11648/j.ijssn.20210901.15 |
| Page(s) | 30-37 |
| Creative Commons |
This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited. |
| Copyright |
Copyright © The Author(s), 2021. Published by Science Publishing Group |
Behavior Detection, Fast moving, Video Surveillance, Object Detection, Object Tracking
| [1] | Hu, Y., Lu, M., & Lu, X. (2019). Feature refinement for image-based driver action recognition via multi-scale attention convolutional neural network. Signal Processing Image Communication, 81. |
| [2] | Hatirnaz, E., Sah, M., & Direkoglu, C. (2020). A novel framework and concept-based semantic search Interface for abnormal crowd behaviour analysis in surveillance videos. Multimedia Tools and Applications, 1-39. |
| [3] | Tripathi, V., Mittal, A., Gangodkar, D., & Kanth, V. (2019). Real time security framework for detecting abnormal events at ATM installations. Journal of Real-time image processing, 16 (2), 535-545. |
| [4] | Dhiman, C., & Vishwakarma, D. K. (2019). A review of state-of-the-art techniques for abnormal human activity recognition. Engineering Applications of Artificial Intelligence, 77, 21-45. |
| [5] | Zhan, C., Duan, X., Xu, S., Song, Z., & Luo, M. (2007). An Improved Moving Object Detection Algorithm Based on Frame Difference and Edge Detection. In Proceedings of the Fourth International Conference on Image and Graphics, 519-523. |
| [6] | Tsai, D. M., & Lai, S. C. (2008). Independent component analysis-based background subtraction for indoor surveillance. IEEE Transactions on image processing, 18 (1), 158-167. |
| [7] | Aslani, S., & Mahdavi-Nasab, H. (2013). Optical flow based moving object detection and tracking for traffic surveillance. International Journal of Electrical, Computer, Energetic, Electronic and Communication Engineering, 7 (9), 1252-1256. |
| [8] | Jocher, G. (2020). Yolov5. Code repository https://github. com/ ultralytics/ yolov5. |
| [9] | He, L., Liao, X., Liu, W., Liu, X., Cheng, P., & Mei, T. (2020). FastReID: a Pytorch toolbox for real-world person re-identification. arXiv preprint arXiv: 2006.02631. |
| [10] | Bochinski, E., Eiselein, V., & Sikora, T. (2017). High-Speed tracking-by-detection without using image information. In 2017 14th IEEE International Conference on Advanced Video and Signal Based Surveillance, 1-6. |
| [11] | Bochinski, E., Senst, T., & Sikora, T. (2018). Extending IOU based multi-object tracking by visual information. In 2018 15th IEEE International Conference on Advanced Video and Signal Based Surveillance, 1-6. |
| [12] | Redmon, J., Divvala, S., Girshick, R., & Farhadi, A. (2016). You only look once: Unified, real-time object detection. In Proceedings of the IEEE conference on computer vision and pattern recognition, 779-788. |
| [13] | Redmon, J., & Farhadi, A. (2018). Yolov3: An incremental improvement. arXiv preprint arXiv: 1804. 02767. |
| [14] | Wang, C. Y., Liao, H. Y. M., Wu, Y. H., Chen, P. Y., Hsieh, J. W., & Yeh, I. H. (2020). CSPNet: A new backbone that can enhance learning capability of CNN. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition workshops, 390-391. |
| [15] | Lin, T. Y., Dollár, P., Girshick, R., He, K., Hariharan, B., & Belongie, S. (2017). Feature pyramid networks for object detection. In Proceedings of the IEEE conference on computer vision and pattern recognition, 2117-2125. |
| [16] | Liu, S., Qi, L., Qin, H., Shi, J., & Jia, J. (2018). Path aggregation network for instance segmentation. In Proceedings of the IEEE conference on computer vision and pattern recognition, 8759-8768. |
APA Style
Zhang Xiaoping, Ji Jiahui, Wang Li, He Zhonghe, Liu Shida. (2021). People’s Fast Moving Detection Method in Buses Based on YOLOv5. International Journal of Sensors and Sensor Networks, 9(1), 30-37. https://doi.org/10.11648/j.ijssn.20210901.15
ACS Style
Zhang Xiaoping; Ji Jiahui; Wang Li; He Zhonghe; Liu Shida. People’s Fast Moving Detection Method in Buses Based on YOLOv5. Int. J. Sens. Sens. Netw. 2021, 9(1), 30-37. doi: 10.11648/j.ijssn.20210901.15
AMA Style
Zhang Xiaoping, Ji Jiahui, Wang Li, He Zhonghe, Liu Shida. People’s Fast Moving Detection Method in Buses Based on YOLOv5. Int J Sens Sens Netw. 2021;9(1):30-37. doi: 10.11648/j.ijssn.20210901.15
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Download@article{10.11648/j.ijssn.20210901.15,
author = {Zhang Xiaoping and Ji Jiahui and Wang Li and He Zhonghe and Liu Shida},
title = {People’s Fast Moving Detection Method in Buses Based on YOLOv5},
journal = {International Journal of Sensors and Sensor Networks},
volume = {9},
number = {1},
pages = {30-37},
doi = {10.11648/j.ijssn.20210901.15},
url = {https://doi.org/10.11648/j.ijssn.20210901.15},
eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ijssn.20210901.15},
abstract = {To ensure the public’s safety such as in buses, it is very important to accurately judge people’s behaviors and give early warnings. If by watching the video surveillance manually, the cost will be very high, and it cannot be effectively popularized, so video automatic monitoring is preferred. For buses, its environmental space is closed as well as narrow, and at the same time, it is often in a non-stationary state, so traditional behavior detection methods cannot be used here as they are easily affected by moving environment and difficult to fulfill object behavior identification in real time. Aiming at this problem, for people’s fast-moving in buses, a kind of detection method based on YOLOv5 is proposed in this paper. Firstly, the method detects people through one-stage object detection. Secondly, in order to obtain the person's movement trajectory quickly and accurately, an improved two-stage object matching algorithm is designed to track different people. Then, the speed curves of a person during normal activities and fast moving are compared. Finally, an abnormal alarm mechanism is constructed to realize the effective fast movement alarm. Surveillance video in the bus was used to test and evaluate the effectiveness of the method. Results show that the accuracy rate of our method can get 95.4%.},
year = {2021}
}
TY - JOUR T1 - People’s Fast Moving Detection Method in Buses Based on YOLOv5 AU - Zhang Xiaoping AU - Ji Jiahui AU - Wang Li AU - He Zhonghe AU - Liu Shida Y1 - 2021/05/20 PY - 2021 N1 - https://doi.org/10.11648/j.ijssn.20210901.15 DO - 10.11648/j.ijssn.20210901.15 T2 - International Journal of Sensors and Sensor Networks JF - International Journal of Sensors and Sensor Networks JO - International Journal of Sensors and Sensor Networks SP - 30 EP - 37 PB - Science Publishing Group SN - 2329-1788 UR - https://doi.org/10.11648/j.ijssn.20210901.15 AB - To ensure the public’s safety such as in buses, it is very important to accurately judge people’s behaviors and give early warnings. If by watching the video surveillance manually, the cost will be very high, and it cannot be effectively popularized, so video automatic monitoring is preferred. For buses, its environmental space is closed as well as narrow, and at the same time, it is often in a non-stationary state, so traditional behavior detection methods cannot be used here as they are easily affected by moving environment and difficult to fulfill object behavior identification in real time. Aiming at this problem, for people’s fast-moving in buses, a kind of detection method based on YOLOv5 is proposed in this paper. Firstly, the method detects people through one-stage object detection. Secondly, in order to obtain the person's movement trajectory quickly and accurately, an improved two-stage object matching algorithm is designed to track different people. Then, the speed curves of a person during normal activities and fast moving are compared. Finally, an abnormal alarm mechanism is constructed to realize the effective fast movement alarm. Surveillance video in the bus was used to test and evaluate the effectiveness of the method. Results show that the accuracy rate of our method can get 95.4%. VL - 9 IS - 1 ER -
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