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Open AccessArticle

STAM-CCF: Suspicious Tracking Across Multiple Camera Based on Correlation Filters

1
Department of Computer Science, Tunghai University, Taichung 40704, Taiwan
2
Electrical Engineering and Computer Science Department (EECS-IGP), National Chiao Tung University, Hsinchu 30010, Taiwan
3
Department of Computer Science, National Chiao Tung University, Hsinchu 30010, Taiwan
*
Author to whom correspondence should be addressed.
Sensors 2019, 19(13), 3016; https://doi.org/10.3390/s19133016
Received: 1 June 2019 / Revised: 25 June 2019 / Accepted: 4 July 2019 / Published: 9 July 2019
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Abstract

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. View Full-Text
Keywords: suspicious tracking; surveillance; multi-camera tracking; feature based tracking suspicious tracking; surveillance; multi-camera tracking; feature based tracking
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Sheu, R.-K.; Pardeshi, M.; Chen, L.-C.; Yuan, S.-M. STAM-CCF: Suspicious Tracking Across Multiple Camera Based on Correlation Filters. Sensors 2019, 19, 3016.

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