Robust Visual Tracking Using Structurally Random Projection and Weighted Least Squares

Abstract

Sparse representation based visual tracking approaches have attracted increasing interests in the community in recent years. The main idea is to linearly represent each target candidate using a set of target and trivial templates while imposing a sparsity constraint onto the representation coefficients. After we obtain the coefficients using L1-norm minimization methods, the candidate with the lowest error, when it is reconstructed using only the target templates and the associated coefficients, is considered as the tracking result. In spite of promising system performance widely reported, it is unclear if the performance of these trackers can be maximised. In addition, computational complexity caused by the dimensionality of the feature space limits these algorithms in real-time applications. In this paper, we propose a real-time visual tracking method based on structurally random projection and weighted least squares techniques. In particular, to enhance the discriminative capability of the tracker, we introduce background templates to the linear representation framework. To handle appearance variations over time, we relax the sparsity constraint using a weighed least squares (WLS) method to obtain the representation coefficients. To further reduce the computational complexity, structurally random projection is used to reduce the dimensionality of the feature space while preserving the pairwise distances between the data points in the feature space. Experimental results show that the proposed approach outperforms several state-of-the-art tracking methods.

Target Tracking Algorithm Based on an Adaptive Feature and Particle Filter

“…To improve the tracking speed and accuracy at the same time, Bao et al [8] improved the algorithms that were proposed by the authors of [6,7], through adding the L 2 regularization term on the coefficients that were associated with the trivial templates in the L 1 norm minimization model, and used the accelerated proximal gradient (APG) method to accelerate the speed of solving sparse coefficients. Zhang et al [17] imposed a weighted least squares technique, which could release the sparsity constraint on the traditional sparse representation methods to achieve strong robustness against appearance variations, and that utilized structurally random projection to reduce the dimensionality of the feature, while improving computational efficiency. Meshgi et al [18] proposed an occlusion-aware particle filter framework by utilizing a binary flag to attach to each particle, in order to estimate the occlusion state according to the state and to treat occlusions in a probabilistic manner.…”

Dense Invariant Feature-Based Support Vector Ranking for Cross-Camera Person Reidentification

“…Some methods are dedicated to exploring those invariant characteristics, and measuring their similarity using a standard distance metric. Since the information related to the cameras is often unreliable, most of the representations are built based on visual appearance features [7], [8], [9]. To make the representations more robust, several types of low level features such as color, shape, texture and interest points are often combined together [10], [11], [12].…”

Visual Tracking Based on Discriminative Compressed Features

“…The former focuses on modeling the appearance of the tracked target and then finds the candidate that is the most similar to the target template as the tracking result. The representative methods include those trackers based on sparse representation [23][24][25][26][27][28][29]. In [29], sparse coding is used to extract features from sampled patches.…”

“…To meet this requirement, in this paper, we adopt compressive sensing to reduce the dimensionality of high-dimensional appearance features. Let ∈ R be the wavelet features and Γ be a random matrix computed using the same method as in [26]. The compressed features V ∈ R can be computed as V = Γ .…”