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Computer Vision and Image Understanding
Volume 100, Issue 3, December 2005, Pages 442-457
 
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doi:10.1016/j.cviu.2005.06.002    How to Cite or Link Using DOI (Opens New Window)
Copyright © 2005 Elsevier Inc. All rights reserved.

The influence of perceptual grouping on motion detection

Qigang Gaoa, Corresponding Author Contact Information, E-mail The Corresponding Author, Yun Zhanga and Alan Parslowb

aFaculty of Computer Science, Dalhousie University, 6050 University Avenue, Halifax, NS, Canada B3H 3J5 bDeep Vision Inc., 33 Ochterloney Street, Suite 125, Dartmouth, NS, Canada B2Y 1E7

Received 12 August 2004; 
accepted 14 June 2005. 
Available online 19 August 2005.

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Abstract

In this paper, we present a perceptual organization-based method for detecting moving objects from image sequences. To achieve the characteristics of real-time, efficiency, and robustness, a perceptual computation model of edge partitioning and grouping was proposed for the extraction of edge traces on the fly. Each edge trace is made up of generic edge tokens (GETs) which are perceptual features, and defined qualitatively based on the principles of Gestalt laws. Motion detection uses two basic computations: (1) segment motion GETs (MGETs) by computing the gradient differences between GET streams in consecutive frames; and (2) detect motion objects by perceptually grouping MGETs into object clusters. The MGETs in each cluster are constrained by the proximity of the features, and the motion continuation of the cluster measured by motion persistence, etc. Experimental results are provided.

Keywords: Motion detection; Perceptual grouping; Generic edge token (GET); Motion GET (MGET) segmentation; MGET cluster; Motion persistence measure

Article Outline

1. Introduction
2. Perceptual partitioning and grouping
2.1. Perceptual organization
2.2. Edge partition and grouping
3. GET-based motion detection algorithm
3.1. GET map extraction
3.2. MGET segmentation
3.3. Object detection by MGET grouping
3.4. Motion object detection using MP measure
4. Experiments
4.1. Using stationary camera
4.2. Using moving camera
4.3. Result comparison
5. Summary
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