Moving Object Detection and Classification Using Neural Network

Dewan, M. Ali Akber; Hossain, M. Julius; Chae, Oksam

doi:10.1007/978-3-540-78582-8_16

Cited by 2 publications

(1 citation statement)

References 7 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…These background edges are removed in the postprocessing step utilizing DE n−1,n and DE n,n+1 with a variability test of matching confidence (DM). After detection, moving edges are grouped together, where each group represents a moving object [20]. Each group of moving edges is used to generate the region of interest (ROI).…”

Section: Overviewmentioning

confidence: 99%

An Adaptive Motion Segmentation for Automated Video Surveillance

Dewan

Hossain

Chae

2008

EURASIP J. Adv. Signal Process.

Self Cite

View full text Add to dashboard Cite

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.

show abstract

Section: Overviewmentioning

confidence: 99%