Object tracking is divided into single-object tracking (SOT) and multi-object tracking (MOT).
MOT aims to maintain the identities of multiple objects across a series of continuous video …

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 …

Multiple pedestrian tracking faces the challenge of tracking pedestrians in the presence of
occlusion. Existing methods suffer from inaccurate motion estimation, appearance feature …

Due to the complexity and clutter of real-world scenes, occlusion becomes a long-lasting
difficulty in object tracking. Most existing tracking methods cannot effectively handle …

The goal of multi-object tracking (MOT) is detecting and tracking all the objects in a scene,
while keeping a unique identifier for each object. In this paper, we present a new robust …

The task of multiple people tracking in monocular videos is challenging because of the
numerous difficulties involved: occlusions, varying environments, crowded scenes, camera …

Multi-object tracking (MOT) has been dominated by the use of track by detection approaches
due to the success of convolutional neural networks (CNNs) on detection in the last decade …

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 …

Applications of multi-object tracking and segmentation (MOTS) include autonomous driving
and video surveillance systems. In these applications, the tracking system must be able to …

In online multi-target tracking, modeling of appearance and geometric similarities between
pedestrians visual scenes is of great importance. The higher dimension of inherent …