GraDED: A graph-based parametric dictionary learning algorithm for event detection
Short-time event detection from videos obtained using handheld or car-mounted cameras is an overarching challenge in surveillance. The problem demands simultaneous spatiotemporal localization of the event along with removal of a dynamic background. Existing state-of-the-art techniques are sensitive to non-uniform jitter, changing background, and clutter. In this paper, we propose graph Laplacian assisted parametric dictionary learning, GraDED to account for the aforementioned variations. The temporal occurrence and duration of the event is determined from weights learned using a dynamic graph, while the spatial localization is performed by graph based dictionary learning. We demonstrate the efficacy of our approach by comparing with three state-of-the-art methods and achieve on average an overall increase of 0.08 in specificity and 0.6 in sensitivity for event detection.