Although sparse representation-based tracking approaches show good performance

they usually fail to observe the object motion because of noise

rotation

partial occlusion

motion blur

and illumination or pose variation. This study proposes an algorithm based on sparse representation and a priori probability of object template to improve tracking capability under partial occlusion

rotation

pose change

and motion blur conditions.An L1 tracker is also developed

which runs in real time and possesses better robustness than other L1 trackers. The importance of the target template is measured by a priori probability and is considered in the proposed algorithm when updating the object template. Combined with the regularization model

a novel sparse representation model of the object is presented. Based on the proposed target appearance model

an effective template update scheme is designed by adjusting the weighs of the target templates. The tracking particles of the current frame are generated by the last tracking result according to the Gaussian distribution. The sparse representation of each particle to the template subspace is obtained by solving the L1-regularized least square problem

and a target searching strategy is employed to find the particle that well matches the template as the tracking result. The particle filter is then used to propagate sample distribution in the next tracking frame. Compared with existing popular tracking algorithms

the proposed algorithm can achieve better tracking performance in diverse test video datasets.Experimental results demonstrate that the proposed algorithm can handle appearance changes

such as pose variation

rotation

illumination

motion blur

and occlusion. Compared with state-of-the-art methods

the proposed algorithm performs well and obtains the best results in the sequences of FaceOcc1

Girl

BlurBody

and Singer1

with average center location errors of 6.8

4.0

16.3

and 3.5 pixels

respectively. The average tracking success rate of the proposed algorithm is high. The tracking accuracy is improved with the proposed minimization model for finding the sparse representation of the target

and the real-time performance is achieved by a new APG-based numerical solver for the resulting L1 norm-related minimization problems. The proposed algorithm can track target robustly and reliably under partial occlusion

rotation

pose variation

and motion blur conditions.A very fast numerical solver based on the accelerated proximal gradient approach is developed to solve the resulting L1 norm-related minimization problem. Qualitative and quantitative evaluations demonstrate that the performance of the proposed algorithm is comparable to that of the state-of-the-art tracker on challenging benchmark video sequences. The proposed method can therefore be used for engineering applications.