A General Framework for Multi-Human Tracking using Kalman Filter and Fast Mean Shift Algorithms

The task of reliable detection and tracking of multiple objects becomes highly complex for crowded scenarios. In this paper, a robust framework is presented for multi-Human tracking. The key contribution of the work is to use fast calculation for mean shift algorithm to perform tracking for the cases when Kalman filter fails due to measurement error. Local density maxima in the difference image - usually representing moving objects - are outlined by a fast non-parametric mean shift clustering procedure. The proposed approach has the robust ability to track moving objects, both separately and in groups, in consecutive frames under some kinds of difficulties such as rapid appearance changes caused by image noise and occlusion.

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