SSPARED: Saliency and sparse code analysis for rare event detection in video
The problem of detecting rare and unusual events in video is critical to the analysis of large video datasets. Such events are identified as those occurrences within a sequence that cause a significant change in the scene. We propose to determine the significance of a frame, while preserving its compact representation, by introducing a saliency-driven dictionary learning technique. The derived sparse codes are then leveraged, together with the Kullback-Leibler divergence, in the design of a histogram-based metric that we use to evaluate the scene changes between consecutive frames. Our method, SSPARED, is compared with two state of the art methods for anomaly detection and shows significant improvement in detecting abnormal incidents and reduced false alarm generation.