Prasannakumar Aswathy, Mishra Deepak
Department of Avionics, Indian Institute of Space Science and Technology, Trivandrum 695547, Kerala, India.
J Imaging. 2024 Jul 16;10(7):171. doi: 10.3390/jimaging10070171.
Recently, to address the multiple object tracking (MOT) problem, we harnessed the power of deep learning-based methods. The tracking-by-detection approach to multiple object tracking (MOT) involves two primary steps: object detection and data association. In the first step, objects of interest are detected in each frame of a video. The second step establishes the correspondence between these detected objects across different frames to track their trajectories. This paper proposes an efficient and unified data association method that utilizes a deep feature association network (deepFAN) to learn the associations. Additionally, the Structural Similarity Index Metric (SSIM) is employed to address uncertainties in the data association, complementing the deep feature association network. These combined association computations effectively link the current detections with the previous tracks, enhancing the overall tracking performance. To evaluate the efficiency of the proposed MOT framework, we conducted a comprehensive analysis of the popular MOT datasets, such as the MOT challenge and UA-DETRAC. The results showed that our technique performed substantially better than the current state-of-the-art methods in terms of standard MOT metrics.
最近，为了解决多目标跟踪（MOT）问题，我们利用了基于深度学习方法的强大功能。基于检测的多目标跟踪（MOT）方法涉及两个主要步骤：目标检测和数据关联。在第一步中，在视频的每一帧中检测感兴趣的目标。第二步是在不同帧之间建立这些检测到的目标之间的对应关系，以跟踪它们的轨迹。本文提出了一种高效且统一的数据关联方法，该方法利用深度特征关联网络（deepFAN）来学习关联。此外，还采用了结构相似性指数度量（SSIM）来解决数据关联中的不确定性，对深度特征关联网络进行补充。这些组合的关联计算有效地将当前检测结果与先前的轨迹联系起来，提高了整体跟踪性能。为了评估所提出的MOT框架的效率，我们对流行的MOT数据集进行了全面分析，如MOT挑战赛和UA-DETRAC。结果表明，在标准MOT指标方面，我们的技术比当前的最先进方法表现得更好。
