Research On Multi-object Detection And Tracking Algorithm Based On Industrial Security Video

With the improvement of people's living standard,people have a higher demand for the convenience and safety of life.In recent years,with the rapid development of artificial intelligence,people see more possibilities in the future.Computer vision has always been an important development direction of artificial intelligence.It gives the eyes of artificial intelligence system to perceive the world.Computer vision is widely used in unmanned driving,intelligent monitoring,safety production and other fields.Among them,target detection and multi-target tracking algorithm are important research directions in the field of computer vision.In the past decades,target detection algorithm and multi-target tracking algorithm have developed from traditional machine learning and artificial feature operators(hog,sift,Kalman filter,etc.)to autonomous learning stage based on deep learning.The accuracy and speed of detection and tracking have been greatly improved.This thesis will focus on the application of target detection algorithm and multi-target tracking algorithm in intelligent security monitoring.In order to ensure the safety of workers and working environment,the factory will put forward the requirements for workers to wear helmets,work clothes and no smoking.Therefore,this paper will specifically study how to achieve the function of helmet wearing detection and personnel tracking through machine learning algorithm.The main research work of this paper is as follows:(1)In this thesis,we propose a new algorithm of helmet detection based on color distribution.In this algorithm,we introduce the CSP(center and scale prediction)pedestrian detection network,which effectively improves the accuracy of helmet detection by combining the color distribution features in the detection frame.The validity of the algorithm is further verified by testing the helmet data set collected on site.(2)This thesis presents a multi-target tracking algorithm based on multi feature fusion of matching matrix.In the improved algorithm,we use deep sort multi-target tracking algorithm as the basic framework,through multi feature fusion,multi angle constraints on the allocation matrix to improve the tracking effect.Through the contrast experiment,it is proved that the matching matrix after multi feature fusion can effectively improve the performance of target tracking.(3)In this thesis,a multi-target tracking algorithm based on optical flow feature fusion is proposed,which can improve the accuracy of multi-target tracking by fusing the optical flow feature and the apparent feature of the target.Specifically,the network takes the DAN(deep affinity network)multi-target tracking network as the backbone network.Through flownet2,the inter frame optical flow information is introduced into the network to achieve the fusion of the target's motion features and apparent features,which effectively improves the accuracy and robustness of multi-target tracking.