Multi-target tracking, a high-level vision job in computer vision, is crucial to understanding
autonomous driving surroundings. Numerous top-notch multi-object tracking algorithms …

The main challenge of Multi-Object Tracking (MOT) lies in maintaining a continuous
trajectory for each target. Existing methods often learn reliable motion patterns to match the …

Recent works have shown that convolutional networks have substantially improved the
performance of multiple object tracking by simultaneously learning detection and …

Multi-object tracking (MOT) aims to associate target objects across video frames in order to
obtain entire moving trajectories. With the advancement of deep neural networks and the …

In recent years, algorithms for multiple object tracking tasks have benefited from great
progresses in deep models and video quality. However, in challenging scenarios like drone …

Object detection plays a vital role in the fields of computer vision, machine learning, and
artificial intelligence applications (such as FUSE‐AI (E‐healthcare MRI scan), face detection …

Significant progress has been achieved in multi-object tracking (MOT) through the evolution
of detection and re-identification (ReID) techniques. Despite these advancements …

The process of object tracking involves consistently identifying each instance across frames
depending on initial set of object detection (s). Moreover, in multiple object tracking (MOT) …

The accuracy of learning-based optical flow estimation models heavily relies on the realism
of the training datasets. Current approaches for generating such datasets either employ …

视觉多目标跟踪是计算机视觉领域的热点问题, 然而, 场景中目标数量的不确定,
目标之间的相互遮挡, 目标特征区分度不高等多种难题导致了视觉多目标跟踪现实应用进展缓慢 …