Thousands of images and videos are collected from construction projects during
construction. These contain valuable data that, if harnessed efficiently, can help automate or …

Object detection and tracking is one of the most important and challenging branches in
computer vision, and have been widely applied in various fields, such as health-care …

Recently, Multi-Object Tracking (MOT) has attracted rising attention, and accordingly,
remarkable progresses have been achieved. However, the existing methods tend to use …

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 …

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 …

We present ARTrack, an autoregressive framework for visual object tracking. ARTrack
tackles tracking as a coordinate sequence interpretation task that estimates object …

3D-LiDAR-based cooperative perception has been generating significant interest for its
ability to tackle challenges such as occlusion, sparse point clouds, and out-of-range issues …

Temporal modeling of objects is a key challenge in multiple-object tracking (MOT). Existing
methods track by associating detections through motion-based and appearance-based …

We present a unified method, termed Unicorn, that can simultaneously solve four tracking
problems (SOT, MOT, VOS, MOTS) with a single network using the same model parameters …

Kalman filter (KF) based methods for multi-object tracking (MOT) make an assumption that
objects move linearly. While this assumption is acceptable for very short periods of …