This paper describes a novel model for training an event detection system based on object tracking. We propose to model the training as a multiple instance learning problem, which allows us to train the classifier from annotated events despite temporal ambiguities. We apply this technique to realize a Perimeter Intrusion Detection (PID) algorithm and employ image-based features to distinguish real objects from moving vegetation and other distractions. An earlier developed tracking system is extended with the proposed technique to create an on-line PID-event detection system. Experiments with challenging videos show a reduction of the number of false positives by a factor 2-3 and improve the F1 detection performance from 0.15 to 0.28, when compared to a commercially available PID algorithm.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.