VIVA-uOttawa / CBSA at TRECVID 2012: Interactive Surveillance Event Detection ∗

Abstract

We present an interactive video event detection system for the TRECVID 2012 Surveillance Event Detection (SED) task [16]. Inspired by previous TRECVID submissions, the underlying approach is built on combining automated detection of temporal regions of interest through the extraction of binary spatio-temporal keypoint descriptors in observed video-sequences (Video Analytics module), and efficient manual filtering of false alarms through the use of a custom-designed graphical user interface (Visual Analytics module). We make the automated detection of temporal regions of interest feasible by using efficient binary feature descriptors. These descriptors allow for descriptor matching in the bag-of-words model to be orders of magnitude faster than traditional descriptors, such as SIFT and optical flow. The approach is evaluated on a single task, PersonRuns, as defined by the TRECVID 2012 guidelines. The combination of Visual Analytics and Video Analytics tools is shown to be essential for the success of a highly challenging task of detecting events of interest in unstructured environments using video surveillance cameras. ∗ This work is done within the CBSA-led PSTP BTS-402 project PROVE-IT(VA) funded by the Defence Research and Developmen