Algorithms Research And Application Based On Multi-object Tracking

In recent years,with the explosive growth of research in the field of computer vision,visual object tracking has an important application in industrial visual recognition tasks.It can assist object detection and improve the speed of recognition,which has a certain theoretical value and research significance.However,in practical application scenarios,multi-object tracking is still faced with problems such as inaccurate tracking,poor robustness and low overall system speed brought about by scene changes.The main research goal of this paper is to realize the multi-object tracking,applied to the UAV needs of practical application scenarios,a scheme of multiple objects tracking algorithm is further study of parallel detection of multi-object tracking algorithm model is put forward to improve accuracy and speed of whole system,This paper proposes that the joint space domain and channel domains attention mechanism to improve the robustness.Contributions of this paper are as follows:(1)A scheme of parallel detection and tracking based on TransformerIn view of the problems of low speed and poor tracking accuracy of the traditional multi-object tracking algorithm,This paper proposes that a parallel detection and tracking scheme model PDT-TR based on transformer.The parallel detector and the former frame tracker are combined to assist the current frame tracking,which achieves high tracking accuracy and greatly reduces the number of False Positive and False Negative tracking.At the same time,the parallel processing of detection and tracking replaces the redundant calculation of detection based tracking algorithm,which improves the speed of the whole tracking system.The experiment results show that the model has achieved the best results in the indexes of MOTA,MOTP,FP and FN,and FN is achieved the optimal one.(2)An attention mechanism of joint space domain and channel domainThis paper analyzed the possible difficulties in UAV tracking scenarios.Aiming at the problems of small target scale and poor tracking effect caused by similar background interference in complex scenes,this paper proposes that an attention mechanism of joint spatial domain and channel domain in the CNN backbone network,which combined the advantages of STN and SENet substructures for feature extraction of image location and channel,and fused the two kinds of feature map.The experiment verifies that the attention mechanism of adding spatial domain and channel domain to the CNN backbone network improves the accuracy and reduces the index of false detection and missed detection.The test results of this model in the UAV datasets achieve robust multi-object tracking in complex scenarios.