Accession Number : AD1037381

Title :   Complex Event Detection via Multi Source Video Attributes (Open Access)

Descriptive Note : Conference Paper

Corporate Author : University of Trento Trento Italy

Personal Author(s) : Ma,Zhigang ; Yang,Yi ; Xu,Zhongwen ; Yan,Shuicheng ; Sebe,Nicu ; Hauptmann,Alexander G

Full Text :

Report Date : 03 Oct 2013

Pagination or Media Count : 7

Abstract : Complex events essentially include human, scenes, objects and actions that can be summarized by helpful for event detection. Many works have exploited attributes at image level for various applications. However, attributes at image level are possibly insufficient for complex event detection in videos due to their limited capability in characterizing the dynamic properties of video data. Hence, we propose to leverage attributes at video level (named as video attributes in this work), i.e., the semantic labels of external videos are used as attributes. Compared to complex event videos, these external videos contain simple contents such as objects, scenes and actions which are the basic elements of complex events. Specifically, building upon a correlation vector which correlates the attributes and the complex event, we incorporate video attributes latently as extra informative cues into the event detector learnt from complex event videos. Extensive experiments on a real-world large-scale dataset validate the efficacy of the proposed approach.

Descriptors :   applied computer science , accuracy , classification , coverings , image recognition , precision , sequences , test and evaluation , factor analysis , multimedia , pattern recognition , learning , machine learning , training , algorithms , artificial intelligence , recognition , detectors , detection , computer vision

Distribution Statement : APPROVED FOR PUBLIC RELEASE