Tuesday, 26 April 2016

MATLAB code of Real-time Object Tracking through Webcam (Robust Mean Shift Tracking with Corrected Background-Weighted Histogram)

Real-Time Object Tracking Through Webcam:

Object tracking is an important task in computer vision. Many algorithms have been proposed to solve the various problems arisen from noises, clutters and occlusions in the appearance model of the target to be tracked. Among various object tracking methods, the mean shift tracking algorithm is a popular one due to its simplicity and efficiency. Mean shift is a non parametric density estimator which iteratively computes the nearest mode of a sample distribution. After it was introduced to the field of computer vision, mean shift has been adopted to solve various problems, such as image filtering, segmentation and object tracking. In the mean shift tracking algorithm, the color histogram is used to represent the target because of its robustness to scaling, rotation and partial occlusion. 

However, the mean shift algorithm is prone to local minima when some of the target features present in the background. Further proposed the background-weighted histogram (BWH) to decrease background interference in target representation. The strategy of BWH is to derive a simple representation of the background features and use it to select the salient components from the target model and target candidate model. More specifically, BWH attempts to decrease the probability of prominent background features in the target model and candidate model and thus reduce the background’s interference in target localization. Such an idea is reasonable and intuitive, and some works have been proposed to follow this idea. One author proposed the object is partitioned into a number of fragments and then the target model of each fragment is enhanced by using BWH. Different from the original BWH transformation, the weights of background features are derived from the differences between the fragment and background colors. Someone proposed the target is represented by combining BWH and adaptive kernel density estimation, which extends the searching range of the mean shift algorithm.

MATLAB Implementation:

Fig: Preview of Webcam Video
Fig: Frame Captured from video stream & left eye is a tracking object
Fig: Tracked left eye with square up to 93 frames.

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