Sheu Ruey-Kai, Pardeshi Mayuresh, Chen Lun-Chi, Yuan Shyan-Ming
Department of Computer Science, Tunghai University, Taichung 40704, Taiwan.
Electrical Engineering and Computer Science Department (EECS-IGP), National Chiao Tung University, Hsinchu 30010, Taiwan.
Sensors (Basel). 2019 Jul 9;19(13):3016. doi: 10.3390/s19133016.
There is strong demand for real-time suspicious tracking across multiple cameras in intelligent video surveillance for public areas, such as universities, airports and factories. Most criminal events show that the nature of suspicious behavior are carried out by un-known people who try to hide themselves as much as possible. Previous learning-based studies collected a large volume data set to train a learning model to detect humans across multiple cameras but failed to recognize newcomers. There are also several feature-based studies aimed to identify humans within-camera tracking. It would be very difficult for those methods to get necessary feature information in multi-camera scenarios and scenes. It is the purpose of this study to design and implement a suspicious tracking mechanism across multiple cameras based on correlation filters, called suspicious tracking across multiple cameras based on correlation filters (STAM-CCF). By leveraging the geographical information of cameras and YOLO object detection framework, STAM-CCF adjusts human identification and prevents errors caused by information loss in case of object occlusion and overlapping for within-camera tracking cases. STAM-CCF also introduces a camera correlation model and a two-stage gait recognition strategy to deal with problems of re-identification across multiple cameras. Experimental results show that the proposed method performs well with highly acceptable accuracy. The evidences also show that the proposed STAM-CCF method can continuously recognize suspicious behavior within-camera tracking and re-identify it successfully across multiple cameras.
在诸如大学、机场和工厂等公共场所的智能视频监控中，对跨多个摄像头的实时可疑跟踪有强烈需求。大多数犯罪事件表明，可疑行为的实施者往往是试图尽可能隐藏自己的不明人员。以往基于学习的研究收集了大量数据集来训练学习模型以跨多个摄像头检测人员，但未能识别新出现的人员。也有一些基于特征的研究旨在在摄像头内跟踪中识别人员。对于那些方法来说，在多摄像头场景和画面中获取必要的特征信息将非常困难。本研究的目的是设计并实现一种基于相关滤波器的跨多个摄像头的可疑跟踪机制，称为基于相关滤波器的跨多个摄像头可疑跟踪（STAM-CCF）。通过利用摄像头的地理信息和YOLO目标检测框架，STAM-CCF调整人员识别，并防止在摄像头内跟踪情况下因目标遮挡和重叠导致信息丢失而产生的错误。STAM-CCF还引入了摄像头相关模型和两阶段步态识别策略来处理跨多个摄像头的重新识别问题。实验结果表明，所提出的方法以高度可接受的准确率表现良好。证据还表明，所提出的STAM-CCF方法能够在摄像头内跟踪中持续识别可疑行为，并在多个摄像头之间成功重新识别。
