Robust object tracking via sparsity-based collaborative model

Abstract

In this paper we propose a robust object tracking algorithm using a collaborative model. As the main challenge for object tracking is to account for drastic appearance change, we propose a robust appearance model that exploits both holistic templates and local representations. We develop a sparsity-based discriminative classifier (SD-C) and a sparsity-based generative model (SGM). In the S-DC module, we introduce an effective method to compute the confidence value that assigns more weights to the foreground than the background. In the SGM module, we propose a novel histogram-based method that takes the spatial information of each patch into consideration with an occlusion handing scheme. Furthermore, the update scheme considers both the latest observations and the original template, thereby enabling the tracker to deal with appearance change effectively and alleviate the drift problem. Numerous experiments on various challenging videos demonstrate that the proposed tracker performs favorably against several state-of-the-art algorithms.

Robust Object Tracking via Multi-block and Sparsity-Based Representation

“…The sample with minimal reconstruction errors is regarded as tracking result. Further, [15] consider the important of the background information. The dictionary is composed of target templates and background templates which overcomes the drawbacks of L1 tracker that provide more discrimination power than the dictionary used in [5].…”

“…The dictionary is composed of target templates and background templates which overcomes the drawbacks of L1 tracker that provide more discrimination power than the dictionary used in [5]. Another advantage of [15] is it selects discriminative features through the classification information which can decrease the dimension of the representation. In [16], they using a linear support vector machine (SVM) to train a discriminative model to separate the target object from the background.…”

“…In current frame, we draw N candidate particle around the tracked result in the previous frame with a particle filter through affine transformation [15]. Each candidate particle divided into K blocks and the block sample is…”

Consistent multi-layer subtask tracker via hyper-graph regularization

“…is the most representative work, and some extensions [5,[29][30][31][32] are developed to improve the l 1 tracker in terms of both speed and accuracy. In [29] , APG based solution is used to improve the l 1 tracker.…”

“…It is an important and fundamental topic in computer vision, and has many applications including intelligent surveillance, motion recognition, robotic, human computer interface (HCI), augmented reality (AR), etc. Although great processes have been made and many tracking algorithms have been proposed [1][2][3][4][5][6][7][8] , it remains an open problem to design a robust tracker in the real-world scenarios due to severe occlusions, large appearance changes, illumination changes, background clutter and abrupt motion, etc.…”