Kuan-Ting Lai, Dong Liu, Ming-Syan, Chen, Shih-Fu Chang. Recognizing Complex Events in Videos by Learning Key Static-Dynamic Evidences. In European Conference on Computer Vision (ECCV), September 2014.
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Complex events in videos consist of various human interactions with different objects in diverse environments. As a consequence, the evidences needed to recognize events may occur in short time periods with variable lengths and may happen anywhere in a video. This fact prevents conventional machine learning algorithms from e ffectively recognizing the events. We propose a novel method that can automatically identify the key evidences in videos for detecting complex events. Both static instances (objects) and dynamic instances (actions) are considered by sampling frames and temporal segments respectively. To compare the characteristic power of heterogeneous instances, we embed static and dynamic instances into a multiple instance learning framework via instance similarity measures, and cast the problem as an Evidence Selective Ranking (ESR) process. We impose L-1 norm to select key evidences while using the Infi nite Push Loss Function to enforce positive videos to have higher detection scores than negative videos. Experiments on large-scale video datasets show that our method can improve the detection accuracy while providing the unique capability in discovering key evidences of each complex event
@InProceedings{ECCV14:Lai,
Author = {Lai, Kuan-Ting and Liu, Dong and Chen, Ming-Syan, and Chang, Shih-Fu},
Title = {Recognizing Complex Events in Videos by Learning Key Static-Dynamic Evidences},
BookTitle = {European Conference on Computer Vision (ECCV)},
Month = {September},
Year = {2014}
}
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