An effective anti-interference visual tracking method

Abstract

Tracking specific objects in images or videos is one of the most attractive problems in visual tasks. It is widely employed in security monitoring, automatic driving, military operations and other scenes. Recently, object tracker based on convolution neural network, especially Siamese network, obtains high accuracy and has been deeply studied. However, in practical application scenarios of visual tracking, when meets clutter background or the object is occluded, the accuracy of the tracking task will drop rapidly, and the tracker loses the target in extreme cases. It is particularly necessary to quickly and accurately relocate the target. Therefore, an anti-interference tracker based on Siamese convolution neural network is developed. Benefiting from the adaptive tracking confidence parameter, once the tracking effect of the tracker has dropped significantly during the tracking process, the location of the object will be corrected immediately. Experimental results show that the proposed method has the ability to relocate and track the target after occlusion or loss effectively.