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arXiv · 2106.03283

Video Imprint

Zhanning Gao·Le Wang·Nebojsa Jojic·Zhenxing Niu·Nanning Zheng·Gang Hua
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Abstract

A new unified video analytics framework (ER3) is proposed for complex event retrieval, recognition and recounting, based on the proposed video imprint representation, which exploits temporal correlations among image features across video frames. With the video imprint representation, it is convenient to reverse map back to both temporal and spatial locations in video frames, allowing for both key frame identification and key areas localization within each frame. In the proposed framework, a dedicated feature alignment module is incorporated for redundancy removal across frames to produce the tensor representation, i.e., the video imprint. Subsequently, the video imprint is individually fed into both a reasoning network and a feature aggregation module, for event recognition/recounting and event retrieval tasks, respectively. Thanks to its attention mechanism inspired by the memory networks used in language modeling, the proposed reasoning network is capable of simultaneous event category recognition and localization of the key pieces of evidence for event recounting. In addition, the latent structure in our reasoning network highlights the areas of the video imprint, which can be directly used for event recounting. With the event retrieval task, the compact video representation aggregated from the video imprint contributes to better retrieval results than existing state-of-the-art methods.

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Zhanning Gao, Le Wang, Nebojsa Jojic, Zhenxing Niu, Nanning Zheng, Gang Hua. 2021-06-07. Video Imprint. https://doi.org/10.1109/tpami.2018.2866114

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