Object tracking is a key issue in computer vision. A tracking algorithm based on compressive sensing theory has a high success rate. However

the efficiency of this algorithm requires further improvement. In addition

this algorithm has to deal with target occlusion. A tracking algorithm based on compressed sensing and particle swarm optimization (PSO) was proposed by focusing on the aforementioned issues. To improve the efficiency of a tracking algorithm based on compressed sensing

the PSO algorithm is incorporated into compression tracking. Furthermore

PSO was chosen over the method that required every other pixel to select target candidates. When a target is occluded

the proposed tracking algorithm can search the total image using PSO. The global search capability of PSO can be efficiently used by the proposed algorithm. This feature can significantly reduce the time required to find the target while improving the anti-occlusion capability of the tracking algorithm based on compressed sensing. The proposed algorithm is implemented on 20 publicly available challenging video sequences

and its performance is evaluated through a comparison with 7 state-of-art methods. The time-consuming process of tracking each frame

the average success rate

and the average deviation of the center position are obtained from the experiment. Experimental results on the 20 video frames show that the proposed algorithm significantly improves tracking efficiency and can adapt to both appearance changes and occlusion. Thus

the tracking success rate is significantly improved. The experimental data indicated that the average success rate reached 65.2 percent at an average of 155.5 frames per second

with an average center position deviation of 33.4. The tracking success rate of the proposed tracking algorithm reached over 85 percent in 9 video sequences

and the center position deviation reached 16 pixels or less in 11 video sequences. Compared with similar algorithms

the average success rate

average center position deviation

and average frames per second of the proposed tracking algorithm are relatively superior. In the compressive tracking algorithm that uses PSO to optimize the calculation of the number of search targets

the calculation count of target classification and recognition is reduced from the original 1 750 times to 120 times. The efficiency of the algorithm is significantly improved. Furthermore

when the target is occluded

the proposed algorithm uses PSO to search the entire image until the target reappears; once the target reappears

the proposed tracking algorithm resumes partial target search. Thus

the search capability of the tracking algorithm when the target is covered is improved. The proposed algorithm can find the reproduction target rapidly and accurately.