Improved Spatio-temporal Context Algorithm For Visual Target Tracking

The visual target tracking is a kind of technology which selects and extracts the features of the target to be tracked firstly.After the description model of the target is set up,the motion information such as the position,velocity,scale,acceleration,etc.of the target is acquired in the input continuous image frames through different algorithm processing secondly.And finally using these information to predict the position of the next frame of the image in order to achieve the purpose of tracking the object automatically and high-level visual analysis.The visual target tracking has been a hot topic in the field of computer vision research.Effective and reliable visual target detection and tracking technology has also been widely used in our real life,such as video surveillance,human-computer interaction,robot vision navigation,vehicle traffic information detection,biometric identification,medical abnormality detection and tracking.Due to the influence from many factors such as the change of target appearance,occlusion problem,low resolution,rapid target movement,ambient environment interference,camera angle change,and illumination change during the target motion,it is difficult to achieve robustness,accuracy,and timely of the visual target tracking method under complex conditions.Efficient and fast visual target tracking algorithms still face enormous challenges.This article focuses on the following issues in the visual target tracking:(1)The paper describes the research status of visual target tracking technology at home and abroad,narrates and analyzes the major difficulties faced by visual target tracking.The classification of target detection and tracking technologies are introduced.Common algorithms for the visual target tracking are introduced.And algorithms based on correlation filtering and deep learning in recent years are also described.(2)An improved spatiotemporal contextual video target tracking algorithm based on multi-feature fusion is proposed.Taking into account the Color Name feature's insensitivity to scale and rotation,the trace effect is compensated by grayscale features.After the color features and grayscale features are extracted,multiplicative fusion is performed,then context prior probabilities are calculated,thereby updating the spatio-temporal model.Experimental results show that the improved algorithm can effectively deal with the tracking problem of in-plane rotation.(3)An improved spatio-temporal context for visual target tracking algorithm based on independent scale filter estimation is proposed.The algorithm uses the fast-scale estimation method of DSST algorithm for reference.A scale filter is introduced in the feature-based spatio-temporal context tracking algorithm.When the position filter obtains the target estimated position,the new target position is used as the center to extract 33 different scales,and then calculates the scaled filter response,finds the maximum response value of the scale filter,and then accurately estimates the tracking target's scale and update it in real time.The experimental results show that the improved algorithm can effectively deal with the tracking problem of scale change.