Abstract:

Object tracking is an active and challenging issue in the field of computer vision.To tackle the problem that a target may suffer from deformation,occlusion and fast motion during the tracking process,a novel Siamese network tracking algorithm is proposed,with emphasis on template updating and trajectory prediction.First,an effective template updating mechanism is introduced to the Siamese network tracking model that adaptively represents the variation of target appearance.This mechanism could further improve the tracking performance when the target suffers from shape or color deformation.Specifically,by analyzing the tracking results of each frame to determine whether the update conditions are met,an adaptive template update strategy is designed,effectively reducing the possibility of template contamination.Second,the Kalman filter is utilized to collect the target position information and predict the motion trajectory.By fusing the object position information predicted by the tracking algorithm in the previous frame with the position information predicted by the trajectory,the cropping position of the search area in the current frame is obtained,which further solves the problem of the object being occluded or moving quickly by combining offline tracking and online learning.Extensive experiments on the VOT2018 and LaSOT datasets verify that the tracking performance of the proposed approach exceeds that obtained by other state-of-the-art algorithms under various complex scenarios.

Key words: deep learning, object tracking, Siamese network, template updating, trajectory prediction, Kalman filtering