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 …

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 …

We propose an online tracking algorithm that performs the object detection and data
association under a common framework, capable of linking objects after a long time span …

Motion-based association for Multi-Object Tracking (MOT) has recently re-achieved
prominence with the rise of powerful object detectors. Despite this, little work has been done …

Exploring robust and efficient association methods has always been an important issue in
multi-object tracking (MOT). Although existing tracking methods have achieved impressive …

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 …

In recent years, algorithms for multiple object tracking tasks have benefited from great
progresses in deep models and video quality. However, in challenging scenarios like drone …

Multi-person tracking is often solved with a tracking-by-detection approach that matches all
tracks and detections simultaneously based on a distance matrix. In crowded scenes …

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 …