Multibandwidth Kernel-Based Object Tracking

Abstract

Object tracking using Mean Shift (MS) has been attracting considerable attention recently. In this paper, we try to deal with one of its shortcoming. Mean shift is designed to find local maxima for tracking objects. Therefore, in large target movement between two consecutive frames, the local and global modes are not the same as previous frames so that Mean Shift tracker may fail in tracking the desired object via localizing the global mode. To overcome this problem, a multibandwidth procedure is proposed to help conventional MS tracker reach the global mode of the density function using any staring points. This gradually smoothening procedure is called Multi Bandwidth Mean Shift (MBMS) which in fact smoothens the Kernel Function through a multiple kernel-based sampling procedure automatically. Since it is important for us to have less computational complexity for real-time applications, we try to decrease the number of iterations to reach the global mode. Based on our results, this proposed version of MS enables us to track an object with the same initial point much faster than conventional MS tracker.

Real-time Object Tracking with Active PTZ Camera using Hardware Acceleration Approach

“…The object trackers based on condensation [21], covariance matrix matching [22], normalized correlation [23], active contours [24]- [25], appearance modeling [26], incremental principal component analysis [27], kernel scheme [28]- [31], and particle filter [32]- [34] can track the object of interest even in the presence of camera motion. But most of these algorithms discussed above for object tracking in dynamic backgrounds are computationally too expensive to be deployed on a moderate computing machine for a practical real-time tracking surveillance application.…”