Parametric tracking of multiple segmented regions
Patrick Sebastian, Yap Vooi Voon, Richard Comley · 2012
This paper proposes a tracking method based on parameters obtained from multiple blobs. The multiple blobs are derived or obtained from segmenting a single blob into multiple blobs or multiple regions that have the same color information where these regions generally remain the same as the target moves. The target being tracked is a person or human walking through the camera view field where the number of regions are dependent on the clothing worn and the general build of the person being tracked. This would indicate that the head, limbs and torso of a person would be segmented into regions of interest. The parameters used in tracking a multiple region or blob target are the vectors between the regions of interest and the mean values of the regions of interest. In this paper, the vector used is derived from the top most region to the lowest region of the multi region target. In addition, the difference between the mean values of the regions is used as a means of tracking in addition to the vector between the regions of interest. The results obtained showed that correct target tracking had consistently higher tracking rate compared to incorrect tracking. Correct tracking rates had higher than 0.9 Track Detection Rate (TDR) rates as compared to incorrect tracking that ranged from 0 to 0.6.