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Event detection have long been a fundamental problem in computer vision society. Various datasets for recognizing human events and activities have been proposed to help developing better models and methods, such as UCF101, HMDB51, etc. These datasets all share the same properties that either predefined scripts are provided or the images are almost actor-oriented with little background noise. These...
As an important branch of multimedia content analysis, Surveillance Event Detection (SED) is still a quite challenging task due to high abstraction and complexity such as occlusions, cluttered backgrounds and viewpoint changes etc. To address the problem, we propose a unified SED detection framework which divides events into two categories, i.e., short-term events and long-duration events. The former...
Accurate pedestrian detection in highly crowded surveillance videos is a challenging task, since the regions of pedestrians in the videos may be largely occluded by other pedestrians. In this paper, we propose an effective part-based deep network cascade (HsNet) to solve this problem. In this model, the part-based scheme effectively restrains the appearance variations of pedestrians caused by heavy...
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