Research On Target Tracking Algorithm For Specific People In Multi-Person Scenes

Target tracking based on computer vision has been applied in intelligent monitoring,intelligent transportation,human-computer interaction,security robots.Single target tracking is to give the location and size of the tracking target in the initial video frame,and then track the target in the next video frame.The target of this paper is people who is a non-rigid body,which is easy to deform relative to rigid body(car,bicycle).Because the direction and size of deformation are unpredictable,it is difficult to use geometric shape to describe people deformation.Multi-person scenarios are more complex than single-person scenarios.People are prone to occlusion in multiperson scenarios,which will reduce the robustness of tracking.In order to solve the above problems,the existing target tracking algorithms are deeply studied in this paper.The main work is as follows:(1)Aiming at the problem of deformation and background interference,a target tracking model based on channel-space attention and multi-layer depth feature fusion is proposed(MLDF-CSA).The model firstly weights the convolution's high-level semantic features and low-level visual features,and then passes the channel-space attention module to give higher weight to the target features,and give the background features lower weights.Multi-loss function training model is composed of logistic loss function and center loss function.Experimental results show that compared with other mainstream tracking algorithms,the proposed MLDF-CSA algorithm has improved the success rate and precision rate of target tracking,which verifies the effectiveness of the proposed method.(2)Tracking algorithm based on selective search(TBSS)is proposed for the problem of target people losing.When the response value of the tracking module MLDF-CSA is lower than the adaptive threshold,the re-detection module is started to detect the target.Selective search is used to obtain candidate boxes for candidate targets,and then candidate boxes are input to support vector machines for positive and negative sample classification.When the maximum response value of positive candidate boxes is greater than the detection confidence,it is the tracking target.The experimental results show that compared with other mainstream tracking algorithms,the TBSS algorithm proposed in this paper has improved the success rate and precision rate of target tracking,which validates the effectiveness of the proposed method.(3)Design and implementation of monocular vision mobile robot tracking system based on TBSS algorithm.The RGB image is acquired using a monocular camera and then denoised.The system uses monocular camera to collect RGB images,and then denoise them to improve the image quality.The system can select the tracking target through visual interface,and then track the selected target.Use the image acquired by the system as a data set for target tracking experiments.Experimental results show that the system can follow a specific target person in a real multiplayer scenario.