Abstract
This article proposes a motion detection method for real-time video analysis. It is the fundamental principle that the parts of the moving objects and the local changes of the images captured by static cameras are strongly correlated. Peak signal-to-noise ratio calculated in a block can characterize the significance of the changes in this area. Moving objects can therefore be detected by thresholding the peak signal-to-noise ratio of the blocks between two adjacent frames. The block-wise scheme used in this frame difference method can explore the local correlation of the movement in both space and time domains. This approach is robust to analyze the video images with noise and high variance caused by environmental changes, such as illuminations changes. Compared with other methods, the proposed method can achieve relatively high detection accuracy with less computation time, where real-time motion detection is available. Experimental results show that the proposed method cost averagely 50% of the running time of ViBe with 3.5% increase of the F-score on detection accuracy.
Highlights
Detection of moving objects from video sequences is a widely studied topic in computer vision
Motion detection is crucial for video surveillance and monitoring (VSAM) systems which involve complex tasks such as object detection, recognition, tracking, and behavior analysis
To achieve real-time motion detection in video sequence, we propose a block-wise frame difference method
Summary
Detection of moving objects from video sequences is a widely studied topic in computer vision. Motion detection is crucial for video surveillance and monitoring (VSAM) systems which involve complex tasks such as object detection, recognition, tracking, and behavior analysis. The detected motion areas reduce the searching space for object detection and tracking. Motion detection extracts motion areas by analyzing video sequences in spatial and/or temporal domain. In the past few decades, various motion detection methods have been proposed. The most popular detection methods can be divided into three groups.[1] One is based on optical flow analysis, one is based on background subtraction, and the other is based on frame difference. The moving objects can be extracted by image subtraction between the current video frame and a reference image known as background.
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