Authors:
Jongmin Yu
;
Jeonghwan Gwak
;
Seongjong Noh
and
Moongu Jeon
Affiliation:
Gwangju Institute of Science and Technology, Korea, Republic of
Keyword(s):
Abnormal Event Detection, Scene Partitioning, Spatio-temporal Feature, Optical Flow.
Related
Ontology
Subjects/Areas/Topics:
Computer Vision, Visualization and Computer Graphics
;
Image and Video Analysis
;
Motion, Tracking and Stereo Vision
;
Optical Flow and Motion Analyses
;
Segmentation and Grouping
;
Video Surveillance and Event Detection
Abstract:
This paper presents a method for detecting abnormal events based on scene partitioning. To develop the practical application for abnormal event detection, the proposed method focuses on handling various activity patterns caused by diverse moving objects and geometric conditions such as camera angles and distances between the camera and objects. We divide a frame into several blocks and group the blocks with similar motion patterns. Then, the proposed method constructs normal-activity models for local regions by using the grouped blocks. These regional models allow to detect unusual activities in complex surveillance scenes by considering specific regional local activity patterns. We construct a new dataset called GIST Youtube dataset, using the Youtube videos to evaluate performance in practical scenes. In the experiments, we used the dataset of the university of minnesota, and our dataset. From the experimental study, we verified that the proposed method is efficient in the complex
scenes which contain the various activity patterns.
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