International Conference on Advanced Technologies, Computer Engineering and Science

Detecting Anomalies in Surveillance Videos with Spatio-Temporal Features

Kadriye Öz Ismail Rakıp Karas

Abstract

One of the purposes of video surveillance systems is to detect anomalies which are unexpected situations at a certain location or at a frame. Anomalies can be related to motion or appearance according to its spatial position. In this paper, we propose an anomaly detection system based on spatio-temporal features. Features from Accelerated Segment Test (FAST) is used for detection of corners location. Optical Flow magnitude and orientation of these points is used as spatio-temporal features. A grid is to the frames to neutralize the effect of proximity to the camera. Normal patterns are clustered with an unsupervised neural network so called Self-Organizing Maps (SOM). In test videos if extracted features cannot model with normal clusters, associated grid cell will be marked as anomaly Keywords - Video Surveillance, Anomaly Detection, Features from Accelerated Segment Test (FAST), Optical Flow, SelfOrganizing Maps (SOM)



Conference
International Conference on Advanced Technologies, Computer Engineering and Science
Keywords
Video Surveillance Anomaly Detection Features from Accelerated Segment Test (FAST) Optical Flow SelfOrganizing Maps (SOM)

Language
English

Subject
Computer Science

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