Sports, Americas, Special Report

[AprpdlNuh]

However, they are not suitable for real world unconstrained video understanding. Most of these works are applied under certain constrains, e.g., only for processing trimmed and/or object-centered video clips. Meanwhile, they usually are specified for certain scenarios, such as person activity, etc. Therefore, such algorithms would fail when being transferred to unconstrained videos on both efficiency and effectiveness. Tube proposal has several drawbacks. First, tube proposals failed to capture the trace of moving objects when cropping the proposals from the original videos. Therefore, learning the activities highly relied on trace would be difficult, e.g. ’vehicle turning right’. Second, the tube proposals still cannot stay away from temporal activity localization to determine the existence of the activities. Inevitably, such methods destroy the completeness of activities. Therefore, it would result in significant degrade of performance. Third, the objects in the tube proposal will suffer from the bounding box shift and distortion across frames, which could result in a high false alarm rate on activity detection.




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