The influence of perceptual grouping on motion detection
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.
perceptual grouping motion detection generic edge token (get) motion get (mget) segmentation mget cluster motion persistence measure