Experiential Sampling for video surveillance
IWVS '03 First ACM SIGMM international workshop on Video surveillance
Critical video quality for distributed automated video surveillance
Proceedings of the 13th annual ACM international conference on Multimedia
Rate-accuracy tradeoff in automated, distributed video surveillance systems
MULTIMEDIA '06 Proceedings of the 14th annual ACM international conference on Multimedia
An evaluation of video-to-video face verification
IEEE Transactions on Information Forensics and Security
Video quality for face detection, recognition, and tracking
ACM Transactions on Multimedia Computing, Communications, and Applications (TOMCCAP)
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Using cameras for detecting hazardous or suspicious events has spurred new research for security concerns. To make such detection reliable, researchers must overcome difficulties such as variations in camera capabilities, environmental factors, imbalances of positive and negative training data, and asymmetric costs of misclassifying events of different classes. Following up on the event-detection framework that we propose in [12], we present in this paper the framework's two major components: invariant feature extraction and biased statistical inference. We report results of our experiments using the framework for detecting suspicious motion events in a parking lot.