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

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 …

The main challenge of Multi-Object Tracking (MOT) lies in maintaining a continuous
trajectory for each target. Existing methods often learn reliable motion patterns to match the …

This article is a survey of deep learning methods for single and multiple sound source
localization, with a focus on sound source localization in indoor environments, where …

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 …

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 …

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 …

Abstract Recently, Multiple Object Tracking has achieved great success, which consists of
object detection, feature embedding, and identity association. Existing methods apply the …

Autonomous robotic systems operating in human environments must understand their
surroundings to make accurate and safe decisions. In crowded human scenes with close-up …