Kernel based Similarity Estimation and Real Time Tracking of Moving Objects

Provided by: International Association of Engineering and Management Education (IAEME)
Topic: Hardware
Format: PDF
In this paper, a traditional mean shift algorithm is simulated for tracking a moving object. Kernel based mean-shift algorithm is used for real and non-real time tracking and it is observed at various moving constraints such as uniformly moving, fast moving, moving with scale change and moving in overlapping of similar objects. The object is initialized in first frame as a candidate when it first appears in video. The algorithm finds the maximum correlation between target and candidate with kernel based density estimation by similarity function of Bhattacharya co-efficient.

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