Robust Visual Tracking Revisited: From Correlation Filter to Template Matching

In this paper, we propose a novel matching based tracker by investigating the relationship between template matching and the recent popular correlation filter based trackers (CFTs). Compared to the correlation operation in CFTs, a sophisticated similarity metric termed "mutual buddies similarity" (MBS) is proposed to exploit the relationship of multiple reciprocal nearest neighbors for target matching. By doing so, our tracker obtains powerful discriminative ability on distinguishing target and background as demonstrated by both empirical and theoretical analyses. Besides, instead of utilizing single template with the improper updating scheme in CFTs, we design a novel online template updating strategy named "memory filtering" (MF), which aims to select a certain amount of representative and reliable tracking results in history to construct the current stable and expressive template set. This scheme is beneficial for the proposed tracker to comprehensively "understand" the target appearance variations, "recall" some stable results. Both qualitative and quantitative evaluations on two benchmarks suggest that the proposed tracking method performs favorably against some recently developed CFTs and other competitive trackers.Comment: has been published on IEEE TI

Sparse models for positive definite matrices

University of Minnesota Ph.D. dissertation. Febrauary 2015. Major: Electrical Engineering. Advisor: Nikolaos P. Papanikolopoulos. 1 computer file (PDF); ix, 141 pages.Sparse models have proven to be extremely successful in image processing, computer vision and machine learning. However, a majority of the effort has been focused on vector-valued signals. Higher-order signals like matrices are usually vectorized as a pre-processing step, and treated like vectors thereafter for sparse modeling. Symmetric positive definite (SPD) matrices arise in probability and statistics and the many domains built upon them. In computer vision, a certain type of feature descriptor called the region covariance descriptor, used to characterize an object or image region, belongs to this class of matrices. Region covariances are immensely popular in object detection, tracking, and classification. Human detection and recognition, texture classification, face recognition, and action recognition are some of the problems tackled using this powerful class of descriptors. They have also caught on as useful features for speech processing and recognition.Due to the popularity of sparse modeling in the vector domain, it is enticing to apply sparse representation techniques to SPD matrices as well. However, SPD matrices cannot be directly vectorized for sparse modeling, since their implicit structure is lost in the process, and the resulting vectors do not adhere to the positive definite manifold geometry. Therefore, to extend the benefits of sparse modeling to the space of positive definite matrices, we must develop dedicated sparse algorithms that respect the positive definite structure and the geometry of the manifold. The primary goal of this thesis is to develop sparse modeling techniques for symmetric positive definite matrices. First, we propose a novel sparse coding technique for representing SPD matrices using sparse linear combinations of a dictionary of atomic SPD matrices. Next, we present a dictionary learning approach wherein these atoms are themselves learned from the given data, in a task-driven manner. The sparse coding and dictionary learning approaches are then specialized to the case of rank-1 positive semi-definite matrices. A discriminative dictionary learning approach from vector sparse modeling is extended to the scenario of positive definite dictionaries. We present efficient algorithms and implementations, with practical applications in image processing and computer vision for the proposed techniques

Robust Visual Tracking Based on an Effective Appearance Model

Also published as a journal article: Lecture Notes in Computer Science, 2008; 5305(2008):396-408Most existing appearance models for visual tracking usually construct a pixel-based representation of object appearance so that they are incapable of fully capturing both global and local spatial layout information of object appearance. In order to address this problem, we propose a novel spatial Log-Euclidean appearance model (referred as SLAM) under the recently introduced Log-Euclidean Riemannian metric [23]. SLAM is capable of capturing both the global and local spatial layout information of object appearance by constructing a block-based Log-Euclidean eigenspace representation. Specifically, the process of learning the proposed SLAM consists of five steps—appearance block division, online Log-Euclidean eigenspace learning, local spatial weighting, global spatial weighting, and likelihood evaluation. Furthermore, a novel online Log-Euclidean Riemannian subspace learning algorithm (IRSL) [14] is applied to incrementally update the proposed SLAM. Tracking is then led by the Bayesian state inference framework in which a particle filter is used for propagating sample distributions over the time. Theoretic analysis and experimental evaluations demonstrate the promise and effectiveness of the proposed SLAM.Xi Li, Weiming Hu, Zhongfei Zhang and Xiaoqin Zhan