human activity localization via sequential change detection
Today's rapid developments in digital media processing capabilities, and network speeds, make the dissemination of multimedia data extremely rapid and reliable, and have attracted significant research attention to video analysis, event detection, tracking and surveillance. In this work, a novel, generally applicable approach to the detection of human activity in video is presented. The areas of activity in the video are first detected via the accumulation and statistical processing of the motion vectors in all frames. The times (frames) at which events begin and end are defined as moments at which the statistical distribution of the motion vectors changes, for each pixel. These time instants are estimated in a novel manner, by applying sequential likelihood ratio testing on the motion vectors of the pixels that have been found to be active. The proposed system provides a theoretically sound solution for the detection of temporal changes in the human (or other) activity in video, without resorting to use of prior knowledge, heuristics, or ad-hoc thresholds. Sequential detection techniques allow us to find the frames where events begin and end, but also allows to pre-define the desired probabilities of false alarm and miss for the system. This is entirely novel for the temporal localization of activities and events in the video processing literature. Finally, sequential change detection methods require the smallest number of samples to detect a change, so they ensure the fastest detection of events. Experiments are performed with real sequences, involving human activities, for varying probabilities of false alarm and miss. Comparison with ground truth results shows that, indeed, the proposed method leads to meaningful localization of events both in time and in space.
activity localization sequential detection video surveillance