Online multi-object tracking aims at generating the trajectories for multiple objects in the
surveillance scene. It remains a challenging problem in crowded scenes because objects …

The deep learning technique has proven to be effective in the classification and localization
of objects on the image or ground plane over time. The strength of the technique's features …

Multi-object tracking (MOT) plays a crucial role in various platforms. Occlusion and insertion
among targets, complex backgrounds and higher real-time requirements increase the …

Unifying object detection and re-identification (ReID) into a single network enables faster
multi-object tracking (MOT), while this multi-task setting poses challenges for training. In this …

Similarity learning has been recognized as a crucial step for object tracking. However,
existing multiple object tracking methods only use sparse ground truth matching as the …

Existing deep multi-object tracking (MOT) approaches first learn a deep representation to
describe target objects and then associate detection results by optimizing a linear …

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 in multiple object tracking use sequence model to calculate the similarity
score between the detections and the previous tracklets. However, the forced exposure to …

Modern multi-object tracking (MOT) system usually involves separated modules, such as
motion model for location and appearance model for data association. However, the …

Despite extensive exploration of more powerful multi-object tracking (MOT) frameworks, the
impact of frequent occlusion has remained a formidable challenge. In this work, we present …