Multi-target Visual Tracking Based on Segmented Histograms and Kalman Filter with Occlusion Handling
Date Issued
2010
Date
2010
Author(s)
Wong, Sio-Sun
Abstract
A multi-target visual tracking approach, which consists of segmented color histograms, position estimation by Kalman filter and occlusion handling, is proposed in this thesis. The moving object regions are extracted by using Su’s Two-Staged Background Subtraction approach. Connected components are located and those of small area are discarded. Color histograms are used as descriptors of an object’s appearance due to their intrinsic benefits of being relatively scale-invariant and posture-invariant. In order to resolve the problem of losing spatial information due to histogram, an object is segmented into the upper and lower parts. Thus, the objects’ appearances are described by six histograms, namely, R, G, B histograms corresponding to the abovementioned two parts. The appearance model is then updated through Exponentially Weighted Moving Average across time. Furthermore, Kalman filter is used for position estimation for each object. We combined the appearance model and position condition to implement the tracking approach. In view of the challenging occlusion problem, a novel approach consists of Merge, Split, Obstructed and Reappeared is proposed. Historical information is utilized such that severe and even full occlusions can be handled. By and large, this approach can handle long, short, full, partial occlusions efficiently for both inter-object occlusion and occlusion by obstructors. Various experiments verify the robustness of our approach.
Subjects
segmented color histograms
Kalman filter
background subtraction
appearance modeling
model update
occlusion handling
Type
thesis
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