Complex event detection by identifying reliable shots from untrimmed videos

Hehe Fan, Xiaojun Chang, De Cheng, Yi Yang, Dong Xu, Alexander G. Hauptmann

Research output: Chapter in Book/Report/Conference proceedingConference PaperResearch

38 Citations (Scopus)


The goal of complex event detection is to automatically detect whether an event of interest happens in temporally untrimmed long videos which usually consist of multiple video shots. Observing some video shots in positive (resp. negative) videos are irrelevant (resp. relevant) to the given event class, we formulate this task as a multi-instance learning (MIL) problem by taking each video as a bag and the video shots in each video as instances. To this end, we propose a new MIL method, which simultaneously learns a linear SVM classifier and infers a binary indicator for each instance in order to select reliable training instances from each positive or negative bag. In our new objective function, we balance the weighted training errors and a l1-l2 mixed-norm regularization term which adaptively selects reliable shots as training instances from different videos to have them as diverse as possible. We also develop an alternating optimization approach that can efficiently solve our proposed objective function. Extensive experiments on the challenging real-world Multimedia Event Detection (MED) datasets MEDTest-14, MEDTest-13 and CCV clearly demonstrate the effectiveness of our proposed MIL approach for complex event detection.

Original languageEnglish
Title of host publicationProceedings - 2017 IEEE International Conference on Computer Vision, ICCV 2017
EditorsRita Cucchiara, Yasuyuki Matsushita, Nicu Sebe, Stefano Soatto
Place of PublicationPiscataway NJ USA
PublisherIEEE, Institute of Electrical and Electronics Engineers
Number of pages9
ISBN (Electronic)9781538610329
ISBN (Print)9781538610336
Publication statusPublished - 2017
Externally publishedYes
EventIEEE International Conference on Computer Vision 2017 - Venice, Italy
Duration: 22 Oct 201729 Oct 2017
Conference number: 16th (Proceedings)


ConferenceIEEE International Conference on Computer Vision 2017
Abbreviated titleICCV 2017
Internet address

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