Objective

2

Visual object tracking is an important research subject in computer vision. It has extensive applications that include surveillance

human-computer interaction

and medical imaging. The goal of tracking is to estimate the states of a moving target in a video sequence. Considerable effort has been devoted to this field

but many challenges remain due to appearance variations caused by heavy occlusion

illumination variation

and fast motion. Low-rank approximation can acquire the underlying structure of a target because some candidate particles have extremely similar appearance. This approximation can prune irrelevant particles and is robust to global appearance changes

such as pose change and illumination variation. Sparse representation formulates candidate particles by using a linear combination of a few dictionary templates. This representation is robust against local appearance changes

e.g.

partial occlusions. Therefore

combining low-rank approximation with sparsity representation can improve the efficiency and effectiveness of object tracking. However

object tracking via low-rank sparse learning easily results in tracking drift when facing objects with fast motion and severe occlusions. Therefore

reverse low-rank sparse learning with a variation regularization-based tracking algorithm is proposed.

Method

2

First

a low-rank constraint is used to restrain the temporal correlation of the objective appearance

and thus

remove the uncorrelated particles and adapt the object appearance change. The rank minimization problem is known to be computationally intractable. Hence

we resort to minimizing its convex envelope via a nuclear norm. Second

the traditional sparse representation method requires solving numerous L

1

optimization problems. Computational cost increases linearly with the number of candidate particles. We build an inverse sparse representation formulation for object appearance using candidate particles to represent the target template inversely

reducing the number of L

1

optimization problems for online tracking from candidate particles to one. Third

variation regularization is introduced to model the sparse coefficient difference. The variation method can model the variable selection problem in bounded variation space

which can restrict object appearance with only a slight difference between consecutive frames but allow the existing difference between individual frames to jump discontinuously

and thus

adapt to fast object motion. Lastly

an online updating scheme based on alternating iteration is proposed for tracking computation. Each iteration updates one variable at a time. Meanwhile

the other variables are fixed to their latest values. To accommodate target appearance change

we also use a local updating scheme to update the local parts individually. This scheme captures changes in target appearance even when heavy occlusion occurs. In such case

the unoccluded local parts are still updated in the target template and the occluded ones are discarded. Consequently

we can obtain a representation coefficient for the observation model and realize online tracking.

Result

2

To evaluate our proposed tracker

qualitative and quantitative analyses are performed using MATLAB on benchmark tracking sequences (occlusion 1

David

boy

deer) obtained from OTB (object tracking benchmark) datasets. The selected videos include many challenging factors in visual tracking

such as occlusion

fast motion

illumination

and scale variation. The experimental results show that when faced with these challenging situations in the benchmark tracking dataset

the proposed algorithm can perform tracking effectively in complicated scenes. Comparative studies with five state-of-the-art visual trackers

namely

DDL (discriminative dictionary learning)

SCM (sparse collaborative model)

LLR (locally low-rank representation)

IST (inverse sparse tracker)

and CNT (convolutional networks training) are conducted. To achieve fair comparison

we use publicly available source codes or results provided by the authors. Among these trackers

DDL

SCM

LLR

and IST are the most relevant. Compared with the CNT tracker

we mostly consider that deep networks have attracted considerable attention in complicated visual tracking tasks. For qualitative comparison

the representative tracking results are discussed on the basis of the major challenging factors in each video. For quantitative comparison

the central pixel error (CPE)

which records the Euclidean distance between the central location of the tracked target and the manually labeled ground truth

is used. When the metric value is smaller

the tracking results are more accurate. From the evolution process of CPE versus frame number

our tracker achieves the best results in these challenging sequences. In particular

our tracker outperforms the SCM

IST

and CNT trackers in terms of occlusion

illumination

and scale variation sequences. It outperforms the LLR

IST

and DDL trackers in terms of fast motion sequences. These results demonstrate the effectiveness and robustness of our tracker to occlusion

illumination

scale variation

and fast motion.

Conclusion

2

Qualitative and quantitative evaluations demonstrate that the proposed algorithm achieves higher precision compared with many state-of-the-art algorithms. In particular

it exhibits better adaptability for objects with fast motion. In the future

we will extend our tracker by applying deep learning to enhance its discriminatory ability.