ESUR: A system for Events detection in SURveillance video

In this paper, we present our eSur (Event detection system on SURveillance video) system, which is derived from TRECVID´09 surveillance tasks. Currently, eSur attempts to detect two categories of events: 1) single-actor events (i.e., PersonRuns and ElevatorNoEntry) irrespective of any interaction between individuals, and 2) pair-activity events (i.e., PeopleMeet, PeopleSplitUp, and Embrace) involves more than one individual. eSur consists of three major stages, i.e., preprocessing, event classification, and post-processing. The preprocessing involves view classification, background subtraction, head-shoulder detection, human body detection and object tracking. Event classification fuses One-vs.-All SVM and rule-based classifiers to identify single-actor and pair-activity events in an ensemble way. To reduce false alarms, we introduce prior knowledge into the post-processing, and in particular, we apply a so-called event merging process over TRECVID dataset. Extensive experiments have been performed over TRECVid´08 and ´09 ED data corpus involving in total 144 hours surveillance video of London Gatwick airport. According to the TRECVid-ED formal evaluation, our prototype has yielded fairly promising results over TRECVid´09 dataset, with top Act.DCR of 1.023, 1.025, 1.02, and 0.334 for PeopleMeet, PeopleSplitUp, Embrace, and ElevatorNoEntry, respectively.