Learning Patterns of Activity Using Real-Time Tracking
IEEE Transactions on Pattern Analysis and Machine Intelligence
Middleware for video surveillance networks
Proceedings of the international workshop on Middleware for sensor networks
Activity Topology Estimation for Large Networks of Cameras
AVSS '06 Proceedings of the IEEE International Conference on Video and Signal Based Surveillance
Shadow detection for moving objects based on texture analysis
Pattern Recognition
Shadow identification and classification using invariant color models
ICASSP '01 Proceedings of the Acoustics, Speech, and Signal Processing, 2001. on IEEE International Conference - Volume 03
Finding camera overlap in large surveillance networks
ACCV'07 Proceedings of the 8th Asian conference on Computer vision - Volume Part I
Proceedings of the 2nd ACM workshop on Multimedia in forensics, security and intelligence
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The use of surveillance cameras to monitor public buildings and urban areas is becoming increasingly widespread. Each camera delivers a continuous stream of video data, which, once archived, is a valuable source of information for forensic analysis. However, current video analysis tools are primarily based on searching backwards and forwards in time at a single location (i.e. camera), which does not account for events or people of interest that change location over time. In this paper we describe a practical system for tracking a target backwards and forwards in both space and time, effectively following a feature of interest as it moves within and between cameras in a surveillance network. This provides a video analysis tool that is target-centred rather than camera-centred, and thus allows rapid access to the footage that matters for forensic analysis.