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

Abstract EN

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.