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Received：20 June 2023，
Revised：2023-09-06，
Published：16 July 2024
移动端阅览
目的
2
多目标跟踪是计算机视觉一个重要的研究方向，为了解决多目标跟踪中错跟和漏跟导致跟踪精度低的问题，提出一种融合目标相似性和作用力的多目标跟踪算法。
方法
2
首先将多目标跟踪问题转化为一个最大后验概率问题，其次将最大后验概率问题映射到网络流中，利用最小代价流寻找最优路径，这样获得的最优路径就是目标轨迹。为了计算网络流中目标节点之间的代价，从以下两方面考虑：1）将目标的外观、运动和位置信息三者结合，计算目标间的相似度；2）考虑目标与目标的相互影响，参考社会力模型中个体之间的吸引力来计算目标节点之间的作用力。
结果
2
在MOT15、MOT16和MOT17共3个公开数据集进行实验评估并与12种方法进行比较，实验结果表明，本文算法在MOTA （multiple object tracking accuracy）、MT （mostly tracked tracklets）、ML （mostly lost tracklets）、FP （false positives）、FN （false negatives）等指标上明显优于OACDASM （online association by continuous-discrete appearance similarity measurement）、STURE （spatial-temporal mutual representation learning）、IQHMOT （identity-quantity harmonic multi-object tracking）和GCNNMatch （graph convolutional neural network match）等典型算法。在MOT15数据集中选取ETH-Bahnhof、TUD-Stadtmitte与PETS09-S2L1 3个视频序列进行消融实验，验证增加目标作用力之后的数据关联结果，消融实验结果表明，增加目标作用力之后可以改善目标跟踪的精度和其他指标，尤其在遮挡不明显的视频序列中。
结论
2
本文在目标多特征的基础之上增加目标节点间作用力，加强了目标间的数据关联，减少错跟的目标数量，有效地提高了目标跟踪的精度。
Objective
2
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
2
First， the multi-object tracking problem is transformed into a maximum a posteriori probability problem. Second， the maximum a posteriori probability problem is mapped to the network flow and the minimum cost flow is used to find the optimal path. To calculate 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 calculate the force between object nodes.
Result
2
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 measurement， spatial-temporal mutual representation learning， identity-quantity harmonic multi-object tracking， graph convolutional 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 association 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
2
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.
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