A critical investigation of recall and precision as measures of retrieval system performance
ACM Transactions on Information Systems (TOIS)
Learning Patterns of Activity Using Real-Time Tracking
IEEE Transactions on Pattern Analysis and Machine Intelligence
Pattern Recognition and Machine Learning (Information Science and Statistics)
Pattern Recognition and Machine Learning (Information Science and Statistics)
Activity Topology Estimation for Large Networks of Cameras
AVSS '06 Proceedings of the IEEE International Conference on Video and Signal Based Surveillance
Tracking people across disjoint camera views by an illumination-tolerant appearance representation
Machine Vision and Applications
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An accurate estimate of camera overlap is a key enabler for efficient network-wide surveillance processing (e.g. inter-camera tracking), especially in large-scale surveillance networks. Techniques based on contradictions in pair-wise occupancy data, such as the exclusion approach, have advantages in robustness and efficiency that make them particularly well suited for large surveillance networks. Correlation techniques share some of these advantages,but have a better understood statistical basis. This paper evaluates a set of contradiction and correlation techniques, using a novel metric, search space precision-recall. This metric reflects the activity-based overlap estimation required for camera handover, such as would be used in inter-camera tracking.Results are reported for a range of networks, including a 24-camera network setup in an office space, where the exclusion estimator showed the best performance.