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We present a fast and robust object tracking algorithm by using 2DPCA and

Visual tracking is one of the fundamental topics in computer vision and plays an important role in numerous researches and practical applications such as surveillance, human computer interaction, robotics, and traffic control. Existing object tracking algorithms can be divided into two categories, that is, discriminative or generative. Discriminative methods treat tracking as a binary classification problem with local search which estimates the decision boundary between an object image patch and the background. Babenko et al. [

Generative methods typically learn a model to represent the target object and incrementally update the appearance model to search for the image region with minimal reconstruction error. Inspired by the success of sparse representation in face recognition [

Recently, many object tracking algorithms have been proposed to exploit the power of subspace representation from different points. Ross et al. [

Motivated by the aforementioned work, this paper presents a robust and fast

Principal component analysis (PCA) is a classical feature extraction and data representation technique widely used in the areas of pattern recognition and computer vision. Compared with PCA, two-dimensional principal component analysis (2DPCA) [

The cost function is set as an

Here, we abandon the trivial templates completely, which makes the target able to be represented by the 2DPCA subspace fully. The error matrix can be obtained by the following equation after we get the projection coefficients matrix

Visual tracking is treated as a Bayesian inference task in a Markov model with hidden state variables. Given a series of image matrices

The proposed tracking algorithm is implemented in MATLAB which runs on a computer with Intel i5-3210 CPU (2.5 GHz) with 4 GB memory. The regularization

To demonstrate the effectiveness of the proposed tracking algorithm, we select six state-of-the-art trackers: the

Sampled tracking results of partial evaluated algorithms on ten challenging sequences.

To conduct quantitative comparisons between the proposed tracking method and the other sate-of-the-art trackers, we compute the difference between the predicted and the ground truth center location error in pixels and overlap rates which are most widely used in quantitative evaluation. The center location error is usually defined as the Euclidean distance between the center locations of tracked objects and their corresponding labeled ground truth. Figure

Center location error (in pixels) evaluation.

Overlap rate evaluation.

The most time consuming part of the generative tracking algorithm is to compute the coefficients using the basis vectors. For the

In this paper, we present a fast and effective tracking algorithm. We first clarify the benefits of the utilizing 2DPCA basis vectors. Then, we formulate the tracking process with the

The authors declare that they have no competing interests.

This project is supported by the Shandong Provincial Natural Science Foundation, China (no. ZR2015FL009).