Integrating similarity and interaction force between objects for multiple object tracking
Objective In the field of computer vision,object tracking is a critical task.Currently,many different types of multi-object tracking algorithms have been proposed,which usually include the following steps:object detection,feature extraction,similarity calculation,data association,and ID assignment.In this process,the object in the video sequence is first detected and a rectangular box is drawn to label the specific object detected.Then,the features of each object are extracted,such as location and appearance features.Then,the similarity of the object is determined by calculating the probability that the object in the adjacent video frames is the same object.Finally,through data association,the objects belonging to the same object in adjacent frames are associated and an ID is assigned to each object precisely.This paper mainly focuses on the feature extraction and data association stage of the object,using combined features to represent the characteristics of the object and then increasing the interaction force between the objects for enhanced data association to address the problem of mistracking in object tracking and thus improve the accuracy of object tracking.Method First,the multi-object tracking problem is transformed into a maximum a posteriori probability problem.Second,the maximum a pos-teriori probability problem is mapped to the network flow and the minimum cost flow is used to find the optimal path.To cal-culate the cost between the object nodes in the network flow,we consider two aspects.First,we calculate the similarity between the objects by combining the appearance,motion,and position information of the objects.Second,we consider the interaction between objects and objects,referring to the attraction between individuals in the social force model to calcu-late the force between object nodes.Result The experimental evaluation on three public datasets MOT15,MOT16,and MOT17 and a comparison with the latest 12 methods show that the proposed algorithm performs well in multiple object tracking accuracy,mostly tracked tracklets,mostly lost tracklets,false positives,false negatives,and other indicators;these indicators are significantly better than those of online association by continuous-discrete appearance similarity mea-surement,spatial-temporal mutual representation learning,identity-quantity harmonic multi-object tracking,graph convo-lutional neural network match(GCNNMatch),and other typical algorithms.Ablation experiments were carried out on three video sequences of TUD-Stadtmitte,ETH-Bahnhof,and PETS09-S2L1 in the MOT15 dataset to verify the data asso-ciation results after increasing the object force.The ablation experimental results show that the object tracking accuracy and other indicators can be improved after increasing the object force,especially in video sequences where occlusion is not obvious.Conclusion In this paper,the force between the target nodes is added based on the target multi-feature,which strengthens the data association between the targets,reduces the number of misfollowed targets,and effectively improves the accuracy of target tracking.
multi-object tracking(MOT)minimum cost flowobject forceobject similaritysocial force model
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