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Single Scale Pixel based LUT Tracker

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Cher Keng Heng, Samantha Yue Ying Lim, Zhiheng Niu, Bo Li
The Visual Object Tracking VOT2013 Challenge in conjunction with ICCV 2013, 2013.12

PLT runs a classifier at a fixed single scale for each test image, to determine the top scoring bounding box which is then the result of object detection. The classifier uses a binary feature vector constructed from color, grayscale and gradient information. To select a small set of discriminative features, an online sparse structural SVM [20] is used. Since the object can be non-rigid and the bounding box may be noisy, not all pixels in the bounding box belong to the object. Hence, a probabilistic object-background segmentation mask from color histograms is created and used to weight the features during SVM training. The resulting weighted and convex problem can be solved in three steps: (i) compute the probability that a pixel belongs to the object by using its color. (ii) solve the original non-sparse structural SVM and (iii) shrink the solution [21], i.e. features with smallest values are discarded. Since the feature vector is binary, the linear classifier can be implemented as a lookup table for fast speed.

Link: https://ieeexplore.ieee.org/document/6755885