Abstract
Detecting abnormal events in crowded scenes is an important but challenging task in computer vision. Contextual information is useful for discovering salient events in scenes; however, it cannot be characterized well by commonly used pixel-based descriptors, such as the HOG descriptor. In this paper, we propose contextual gradients between two local regions and then construct a histogram of oriented contextual gradient (HOCG) descriptor for abnormal event detection based on the contextual gradients. The HOCG descriptor is a distribution of contextual gradients of sub-regions in different directions, which can effectively characterize the compositional context of events. We conduct extensive experiments on several public datasets and compare the experimental results using state-of-the-art approaches. Qualitative and quantitative analysis of experimental results demonstrate the effectiveness of the proposed HOCG descriptor.
Highlights
As one of the key technologies in intelligent video sequence, abnormal event detection (AED) has been actively researched in computer vision due to the increasing concern regarding public security and safety [1]
4 Results and discussion We conduct experiments on different public datasets to evaluate the performances of AED approaches using the histogram of oriented contextual gradient (HOCG) descriptor
We construct a 2D HOCG descriptor for each event, i.e., the spatial direction range is quantized into 8 directions with each direction being 45°
Summary
As one of the key technologies in intelligent video sequence, abnormal event detection (AED) has been actively researched in computer vision due to the increasing concern regarding public security and safety [1]. A large number of cameras have been deployed in many public locations, such as campuses, shopping malls, airports, railway stations, subway stations, and plazas. Traditional video surveillance systems rely on a human operator to monitor scenes and find unusual or irregular events by observing monitor screens. Watching surveillance video is a labor-intensive task. Significant efforts have been devoted to AED in video surveillance, and great progress has been made in recent years, which can free operators from exhausting and tedious tasks and thereby significantly save on labor costs.
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