Object tracking process is a key step in intelligent surveillance. A new object tracking algorithm based on perception hash is proposed to solve the problem of losing a target caused by mutual occlusion and relatively large scale change of tracking a target

as well as reduce the computational complexity of the traditional tracking algorithms based on template matching. The proposed algorithm introduces perception hash to help in the tracking. Perception hash is a one-way map from a multimedia presentation to a perceptual hash code

which means that the multimedia data that contain similar perceptual contents will result in the same hash code and that multimedia data with different perceptual content will result in different hash codes. Perceptual robustness and security make the perception hash reliable for image identification

retrieval

and authentication. Perception hash is used to generate the hash codes of template images and of foreground target images. Hash code is used for matching. In our algorithm

the hash code is abstracted from the DCT (discrete cosine transform) coefficients of an image and is a binary string. In this paper

we use hamming distance between the hash codes of template images and of foreground target images to distinguish similarities. When tracking

the new algorithm searches for the most similar object as the optimal match template for each moving target in the upcoming frame

and the objects that marched are the foreground regions detected by VIBE. The tracking process is called matching strategy. The optimal match template found in the matching strategy records the accurate position and dimension information of a movingtorger

but fails to track the targets with similar perceptual content. To overcome the weakness of the matching strategy

a searching strategy is designed for further tracking. The searching strategy searches the most similar image region surrounding the moving target to be tracked with the use of diamond search. Subsequently

we divide a rectangular area five times as big as the former one

and then carry out the matching strategy again to get an optimal match template. The accuracy of the optimal match template obtained in each frame is verified by the template evaluation function designed in the proposed algorithm. In addition

an adaptive template update strategy is designed for continuous tracking. The template evaluation function and update strategy ensure good adaptability against occlusion and variation of targets when tracking.Compared with the NCC

mean-shift

and the compressive tracking algorithm

the proposed algorithm is more robust when the target is occluded or has large scale change. In addition

the proposed algorithm has lower computational complexity and time cost is reduced by 6.2%

6.3%

and 9.3%

respectively

compared with the three algorithms previonsly mentioned. The proposed object tracking algorithm features better adaptability against occlusion and variation of targets when tracking and has lower time cost. The algorithm can help to build a real-time tracking system.