Title

Data Mining: Applying centroid based adjustment to kernel based object tracking for improving localization

Abstract/Description

In recent studies kernel based object tracking (KBOT) using Bhattacharya coefficient as similarity measure is shown to be robust and efficient object tracking technique. Image histogram provides a compact summarization of the distribution of data in an image. Due to computational efficiency; histogram has been successfully applied in KBOT based tracking algorithms. However without spatial or shape information, similar objects of different color may be indistinguishable based solely on histogram comparisons. The application of meanshift algorithm (the core of KBOT) on 1-D low level features of histogram may converge to false local maxima and cause inaccuracy of target localization. In this paper we presented a robust and efficient tracking approach using structural features along with histogram based Bhattacharya coefficient similarity measure for tracking non rigid objects. It is proposed that integrating the edge based target information as post processing step for updating estimated mean shift centroid in KBOT improves the localization problem. Experimental results show the updated algorithm has achieve more precise tracking results as compared to original kernel based object tracking.

Session Theme

Data Mining

Session Type

Other

Session Chair

Dr. Sajjad Haider

Start Date

15-8-2009 6:15 PM

End Date

15-8-2009 6:35 PM

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Aug 15th, 6:15 PM Aug 15th, 6:35 PM

Data Mining: Applying centroid based adjustment to kernel based object tracking for improving localization

In recent studies kernel based object tracking (KBOT) using Bhattacharya coefficient as similarity measure is shown to be robust and efficient object tracking technique. Image histogram provides a compact summarization of the distribution of data in an image. Due to computational efficiency; histogram has been successfully applied in KBOT based tracking algorithms. However without spatial or shape information, similar objects of different color may be indistinguishable based solely on histogram comparisons. The application of meanshift algorithm (the core of KBOT) on 1-D low level features of histogram may converge to false local maxima and cause inaccuracy of target localization. In this paper we presented a robust and efficient tracking approach using structural features along with histogram based Bhattacharya coefficient similarity measure for tracking non rigid objects. It is proposed that integrating the edge based target information as post processing step for updating estimated mean shift centroid in KBOT improves the localization problem. Experimental results show the updated algorithm has achieve more precise tracking results as compared to original kernel based object tracking.