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In this paper, we address the problem of action detection in unconstrained video clips. Our approach starts from action detection on object proposals at each frame, then aggregates the frame-level detection results belonging to the same actor across the whole video via linking, associating, and tracking to generate action tubes that are spatially compact and temporally continuous. To achieve the target, a novel action detection model with two-stream architecture is firstly proposed, which utilizes the fused feature from both appearance and motion cues and can be trained end-to-end. Then, the association of the action paths is formulated as a maximum set coverage problem with the results of action detection as a priori. We utilize an incremental search algorithm to obtain all the action proposals at one-pass operation with great efficiency, especially while dealing with the video of long duration or with multiple action instances. Finally, a tracking-by-detection scheme is designed to further refine the generated action paths. Extensive experiments on three validation datasets, UCF-Sports, UCF-101 and J-HMDB, show that the proposed approach advances state-of-the-art action detection performance in terms of both accuracy and proposal quality. [ABSTRACT FROM AUTHOR]

In this paper, an abnormal event detection approach inspired by the saliency attention mechanism of human visual system is presented. Conventionally, statistics-based methods suffer from visual scale, complexity of normal events and insufficiency of training data, for the reason that a normal behavior model established from normal video data is used to detect unusual behaviors with an assumption that anomalies are events with rare appearance. Instead, we make the assumption that anomalies are events that attract human attention. Temporal and spatial anomaly saliency are considered consistently by representing the pixel value in each frame as a quaternion, with weighted components that composed of intensity, contour, motion-speed and motion-direction feature. For each quaternion frame, Quaternion Discrete Cosine Transformation (QDCT) and signature operation are applied. The spatio-temporal anomaly saliency map is developed by inverse QDCT and Gaussian smoothing. By multi-scale analyzing, abnormal events appear at those areas with high saliency score. Experiments on typical datasets show that our method can achieve high accuracy results. [ABSTRACT FROM AUTHOR]