An Adaptive Motion Segmentation for Automated Video Surveillance

Abstract

This paper presents an adaptive motion segmentation algorithm utilizing spatiotemporal information of three most recent frames. The algorithm initially extracts the moving edges applying a novel flexible edge matching technique which makes use of a combined distance transformation image. Then watershed-based iterative algorithm is employed to segment the moving object region from the extracted moving edges. The challenges of existing three-frame-based methods include slow movement, edge localization error, minor movement of camera, and homogeneity of background and foreground region. The proposed method represents edges as segments and uses a flexible edge matching algorithm to deal with edge localization error and minor movement of camera. The combined distance transformation image works in favor of accumulating gradient information of overlapping region which effectively improves the sensitivity to slow movement. The segmentation algorithm uses watershed, gradient information of difference image, and extracted moving edges. It helps to segment moving object region with more accurate boundary even some part of the moving edges cannot be detected due to region homogeneity or other reasons during the detection step. Experimental results using different types of video sequences are presented to demonstrate the efficiency and accuracy of the proposed method.

A flexible edge matching technique for object detection in dynamic environment

“…Figure 6 illustrates the procedure of matching value computation. The left matrix shows a distance image, where the shaded points represent the edge pixels of a segment in AREL, and rest of the points show the relative distances (2). In this example, the computed matching value is 0.91287.…”

“…Thus, processing of an edge-image is much faster than the processing a grayscale-image. These features attract many researchers to make use of edge information in various applications including moving object detection, tracking, image change detection and target recognition [2]. Still, in a video sequence, an edge undergoes some variations in consecutive frames due to various factors including noise, quantization error and illumination change [3,4].…”

Segmentation algorithm via Cellular Neural/Nonlinear Network: implementation on Bio-inspired hardware platform

“…In this regard, segmentation is essentially the first step toward many image analysis and computer vision problems [1][2][3][4][5][6][7][8][9][10][11][12][13][14][15]. With the recent advances in several new multimedia applications, there is the need to develop segmentation algorithms running on efficient hardware platforms [16][17][18].…”