Visual object tracking based on perceptual hash algorithm

Visual object tracking (VOT) is one of the most challenging problems in the field of computer vision, which plays a crucial role in many applications, especially in human-computer interaction, surveillance and robotics. This paper investigates the contribution of perceptual hash algorithm (PHA) in visual object tracking, which is to generate a ?fingerprint? string for each image, and then compare the fingerprint of different images. The results are closer, which means the pictures are more similar. We use the hash fingerprint to conduct the object tracking. Our results suggest that perceptual hash algorithm attributes provide superior performance for visual object tracking, and effectively improve the image match.