Moving object detection using region tracking

Song, Eun Young; Lee, Ju-Jang

doi:10.1007/s10015-004-0282-z

Cited by 1 publication

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“…All these methods use the anisotropic kernel for the mean shift to track target in terms of the color information to segment objects from background. Due to different illumination and view resulting in different color information, and lacking of space information of tracking objects, a fully object may be separated into multiple parts [11]. Moreover, mean shift-based tracking may drift away or lose tracking object through frames, especially in the presence of object occlusions [8], or when the foreground object appears in similar color distributions to the background.…”

Section: Introductionmentioning

confidence: 99%

Tracking multi-objects using combination feature and Mean Shift

Wang

Liu

Gong

2012

2012 9th International Conference on Fuzzy Systems and Knowledge Discovery

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This paper proposes a novel framework for tracking multiple targets with occlusion and illumination changing in scenes. Under our framework, the extension of Mean Shift algorithm via five global and local features: central location, width, height, area and Harris corner information, and Mean Shift (CFMS) are used to track multiple targets. To effectively track multiple objects with occluded and don't split up the object to different part, Harris corner information as local feature is extracted by proposed double threshold Harris corner detection algorithm when there exist occluding, moreover classify the corners in the occluding region by K-NN classifier. The proposed CFMS algorithm achieved tracking of multiple targets based on feature fusion and mean shift algorithm. The experimental results have showed that the proposed method of CFMS can track multiple objects more robustly.

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Section: Introductionmentioning

confidence: 99%