標題: STAM-CCF: Suspicious Tracking Across Multiple Camera Based on Correlation Filters
作者: Sheu, Ruey-Kai
Pardeshi, Mayuresh
Chen, Lun-Chi
Yuan, Shyan-Ming
交大名義發表
資訊工程學系
National Chiao Tung University
Department of Computer Science
關鍵字: suspicious tracking;surveillance;multi-camera tracking;feature based tracking
公開日期: 1-七月-2019
摘要: 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.
URI: http://dx.doi.org/10.3390/s19133016
http://hdl.handle.net/11536/152665
ISSN: 1424-8220
DOI: 10.3390/s19133016
期刊: SENSORS
Volume: 19
Issue: 13
起始頁: 0
結束頁: 0
顯示於類別:期刊論文