Low-Rank Representation-Based Object Tracking Using Multitask Feature Learning with Joint Sparsity

Hyuncheol Kim,Joonki Paik

doi:10.1155/2014/147353

Abstract

We address object tracking problem as a multitask feature learning process based on low-rank representation of features with joint sparsity. We first select features with low-rank representation within a number of initial frames to obtain subspace basis. Next, the features represented by the low-rank and sparse property are learned using a modified joint sparsity-based multitask feature learning framework. Both the features and sparse errors are then optimally updated using a novel incremental alternating direction method. The low-rank minimization problem for learning multitask features can be achieved by a few sequences of efficient closed form update process. Since the proposed method attempts to perform the feature learning problem in both multitask and low-rank manner, it can not only reduce the dimension but also improve the tracking performance without drift. Experimental results demonstrate that the proposed method outperforms existing state-of-the-art tracking methods for tracking objects in challenging image sequences.

Highlights

Object tracking is one of the well-known problems in computer vision with many applications including intelligent surveillance, human-computer interface, and motion analysis
A novel incremental alternating direction method- (ADM-) based low-rank optimization strategy is efficiently applied for update of sparse error and features
The proposed method is compared with a number of state-of-the-art tracking algorithms such as SMTT [15], APGL1 [14], SPCA [21], ASL [2], ILRF [18], and RMTT [16]

Summary

Introduction

Object tracking is one of the well-known problems in computer vision with many applications including intelligent surveillance, human-computer interface, and motion analysis. Sparse representation-based l1 norm minimization methods have been successfully employed for object tracking [9,10,11,12,13], where an object is represented as one of multiple candidates in the form of sparse linear combination of a dictionary that can be updated to maintain the optimal object appearance model. The LRST is improved with incremental learning method of low-rank features [18] and adaptive pruning with exploiting temporal consistency [19] In spite of these improvements in MTT and LRST in the particle filter framework, the computational cost increases with the number of particles. To solve the above mentioned problems, we propose a novel object tracking algorithm based on multitask feature learning using joint sparsity and low-rank representation. A novel incremental alternating direction method- (ADM-) based low-rank optimization strategy is efficiently applied for update of sparse error and features. The low-rank optimization problem for learning multitask features can be achieved by a few sequences of efficient closed form updating operation for the optimal state variables of object tracking

Low-Rank Representation of Object with Joint Sparsity

Experimental Results

Conclusion