A large-scale benchmark dataset for event recognition in surveillance video


We introduce a new large-scale video dataset designed to assess the performance of diverse visual event recognition algorithms with a focus on continuous visual event recognition (CVER) in outdoor areas with wide coverage. Previous datasets for action recognition are unrealistic for real-world surveillance because they consist of short clips showing one action by one individual [15, 8]. Datasets have been developed for movies [11] and sports [12], but, these actions and scene conditions do not apply effectively to surveillance videos. Our dataset consists of many outdoor scenes with actions occurring naturally by non-actors in continuously captured videos of the real world. The dataset includes large numbers of instances for 23 event types distributed throughout 29 hours of video. This data is accompanied by detailed annotations which include both moving object tracks and event examples, which will provide solid basis for large-scale evaluation. Additionally, we propose different types of evaluation modes for visual recognition tasks and evaluation metrics along with our preliminary experimental results. We believe that this dataset will stimulate diverse aspects of computer vision research and help us to advance the CVER tasks in the years ahead.

DOI: 10.1109/CVPR.2011.5995586

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@inproceedings{Oh2011ALB, title={A large-scale benchmark dataset for event recognition in surveillance video}, author={Sangmin Oh and Anthony Hoogs and A. G. Amitha Perera and Naresh P. Cuntoor and Chia-Chih Chen and Jong Taek Lee and Saurajit Mukherjee and Jake K. Aggarwal and Hyungtae Lee and Larry S. Davis and Eran Swears and Xiaoyang Wang and Qiang Ji and Kishore K. Reddy and Mubarak Shah and Carl Vondrick and Hamed Pirsiavash and Deva Ramanan and Jenny Yuen and Antonio Torralba and Bi Song and Anesco Fong and Amit K. Roy-Chowdhury and Mita Desai}, booktitle={CVPR}, year={2011} }