Visual tracking is an important field in computer vision and is applied in various domains. Although numerous visual tracking methods have been developed in the past several decades

many challenging issues (e.g.

occlusions

illumination changes

and background clutter) still affect the tracking performance of these methods. Inspired by sparse representation applied in face recognition

the L1 tracker based on sparse representation was proposed by Mei et al. The L1 tracker has good robustness toward partial occlusion but is prone to model drift and time consuming. To address these two problems

this study proposes a tracking method based on discriminative sparse representation. Considering the interference of background and occlusion information

a discriminative sparse representation model is proposed. The proposed model uses the sparseness of the coefficients associated with target and background templates so that the candidate targets can be represented accurately. The sparseness of the coefficients associated with trivial templates makes the proposed tracker robust to partial occlusion. By using the coefficients associated with trivial and target templates

the observation likelihood model

which is adopted in this study

eliminates the interference of the background information and leads to improved tracking results. A fast sparse representation algorithm is designed to increase the tracking speed and used to calculate the coefficients of the discriminative sparse representation model. At the first stage

the proposed algorithm uses the learned iterative shrinkage and thresholding algorithm (LISTA) to calculate the coefficients associated with target templates. At the second stage

the proposed algorithm uses the soft shrinkage operator to calculate the coefficients associated with trivial templates. Based on block coordinate optimization theory

the above optimization procedure is iteratively used to obtain excellent sparse representation coefficients. Under the particle filter framework

the tracking task is accomplished with the proposed model and the fast solution algorithm. The proposed tracker is tested on eight sequences

namely

FaceOcc1

FaceOcc2

David3

Dudek

Singer1

Car4

Jumping

and CarDark. The strength of the proposed tracker is analyzed by comparing the proposed tracker with L1

L1APG (L1 tracker based on accelerated proximal gradient)

SP

and L1L2 trackers. The issues in these sequences include occlusion

in-plane rotation

out-plane rotation

target appearance variations

illumination changes

camera motion

scale changes

motion blur

and background clutter. The selected state-of-the-art trackers

which are used to demonstrate the effectiveness of the proposed tracker

are all based on sparse representation. L1APG tracker

SP tracker

and L1L2 tracker are improvements of the L1 tracker. For robustness evaluation

qualitative and quantitative experiments are conducted to evaluate the proposed tracker. The qualitative comparison shows that the proposed tracker overcomes various challenging issues during tracking. For the quantitative comparison

a precision plot is used to analyze the performance of the proposed tracker. With the location threshold varying from 0 to 50 pixels

the precision plot of the proposed tracker is better than that of the others in the eight sequences. In terms of computing speed

the proposed algorithm can significantly reduce the computational cost of sparse representation. The time for solving an image patch is 0.152 s and 0.257 s for patches with resolutions of 16×16 and 32×32

respectively. The proposed tracker consumes less time than the others in the experiment. Compared with other trackers that do not adopt background templates to construct the sparse representation model

the proposed tracker produces better tracking results. The proposed tracker is more robust to occlusion and other challenges

such as background clutter and appearance changes

and has better tracking speed than the state-of-the-art trackers. Thus

trackers based on the proposed method can be used for many engineering applications

such as video surveillance

medical diagnosis

and athletics. The adopted method

which is used to update the target templates

has low time consumption but may sometimes bring some interference information to the trackers. Thus

a more effective method of updating target templates needs to be developed in the future.