Deep learning has been acknowledged as being robust in managing and controlling the
performance of construction safety. However, there is an absence of state-of-the-art review …

Multi-object tracking (MOT) aims at estimating bounding boxes and identities of objects in
videos. Most methods obtain identities by associating detection boxes whose scores are …

Accurate and robust visual object tracking is one of the most challenging and fundamental
computer vision problems. It entails estimating the trajectory of the target in an image …

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 …

Modern multi-object tracking (MOT) systems usually build trajectories through associating
per-frame detections. However, facing the challenges of camera motion, fast motion, and …

Occlusion between different objects is a typical challenge in Multi-Object Tracking (MOT),
which often leads to inferior tracking results due to the missing detected objects. The …

Discriminative representation is essential to keep a unique identifier for each target in
Multiple object tracking (MOT). Some recent MOT methods extract features of the bounding …

Multi-object tracking (MOT) requires detecting and associating objects through frames.
Unlike tracking via detected bounding boxes or center points, we propose tracking objects …

Transformers have proven superior performance for a wide variety of tasks since they were
introduced. In recent years, they have drawn attention from the vision community in tasks …

Discriminative representation is crucial for the association step in multi-object tracking.
Recent work mainly utilizes features in single or neighboring frames for constructing metric …