Action Localization in Video using a Graph-based Feature Representation
Refereed Conference Meeting Proceeding
We propose a new framework for human action localization in video sequences. The option to not only detect but also localize actions in surveillance video is crucial to improving system’s ability to manage high volumes of CCTV. In the approach, the action localization task is formulated the maximum-path finding problem in the directed spatiotemporal video-graph. The graph is constructed on the top of frame and temporal-based low-level features. To localize actions in the video-graph, we apply a maximum-path algorithm to find the path in the graph that is considered to be the localized action in the video. The proposed approach achieves competitive performance with the J-HMDB and the UCF-Sports dataset.
IEEE International Conference on Advanced Video and Signal based Surveillance.
Proceedings of IEEE International Conference on Advanced Video and Signal based Surveillance.
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Dublin City University (DCU)
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