Performance of optical flow techniques
International Journal of Computer Vision
Pfinder: Real-Time Tracking of the Human Body
IEEE Transactions on Pattern Analysis and Machine Intelligence
The Recognition of Human Movement Using Temporal Templates
IEEE Transactions on Pattern Analysis and Machine Intelligence
System Description: Embedding Verification into Microsoft Excel
CADE-17 Proceedings of the 17th International Conference on Automated Deduction
Real-time closed-world tracking
CVPR '97 Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition (CVPR '97)
Using Adaptive Tracking to Classify and Monitor Activities in a Site
CVPR '98 Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
Adaptive Change Detection for Real-Time Surveillance Applications
VS '00 Proceedings of the Third IEEE International Workshop on Visual Surveillance (VS'2000)
Mobile video surveillance with low-bandwidth low-latency video streaming
Proceedings of the international workshop on Workshop on mobile video
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In this paper, we study the important issues in the design of an efficient wireless real-time visual surveillance system (WISES). Two important considerations are to minimize: (1) the video workload on the wireless network; and (2) the processing workload at the front-end video capturing unit. To achieve the first objective, we propose a cooperative framework for semantic filtering of video frames instead of forwarding every video frame to the back-end server for analysis and monitoring query evaluation. To minimize the processing workload at the front-end unit, a hierarchical object model (HOM) is designed to model the status of the objects, and their temporal and spatial properties in the video scene. With the information provided from the back-end server, the front-end unit pre-analyses the current status of the objects in the HOM by comparing the selection conditions in the submitted monitoring queries following the adaptive object-based evaluation (APOBE) scheme which is proposed to reduce the processing workload at the front-end unit. In APOBE, a higher evaluation frequency is given to the object which is closer to satisfy the condition in the monitoring queries. The performance of WISES has been studied to demonstrate the efficiency of the proposed scheme.