Tracking pedestrians in crowded scenes is a challenging task. Existing transformer-based
tracking methods integrate detection and tracking into a unified model, which simplifies the …

With the rapid development of deep learning, object detection and tracking play a vital role
in today's society. Being able to identify and track all the pedestrians in the dense crowd …

While people tracking has been greatly improved over the recent years, crowd scenes
remain particularly challenging for people tracking due to heavy occlusions, high crowd …

Most multi-object tracking methods have achieved good results in tracking multiple
pedestrians with Kalman filter, but their tracking performance in crowded scenes is still poor …

People tracking in crowded real-world scenes is challenging due to frequent and long-term
occlusions. Recent tracking methods obtain the image evidence from object (people) …

Aiming at the problems of frequent identity switches (IDs) and trajectory interruption of multi-
pedestrian tracking algorithms in dense scenes, this paper proposes a multi-pedestrian …

We propose a new model-free approach for crowd tracking that integrates low-level keypoint
tracking, midlevel patch tracking, and high-level group evolution in one unified framework …

In recent years, several methods and datasets have been proposed to push the performance
of pedestrian detection in crowded scenarios. In this study, three crowd-specific detectors …

Recently, advances in detection and re-identification techniques have significantly boosted
tracking-by-detection-based multi-pedestrian tracking (MPT) methods and made MPT a …

Multi-pedestrian trackers perform well when targets are clearly visible making the
association task quite easy. However, when heavy occlusions are present, a mechanism to …