Deep learning-based object detectors have driven notable progress in multi-object tracking
algorithms. Yet, current tracking methods mainly focus on simple, regular motion patterns in …

In this work, we propose TransTrack, a simple but efficient scheme to solve the multiple
object tracking problems. TransTrack leverages the transformer architecture, which is an …

In this paper, we address various challenges in multi-pedestrian and vehicle tracking in high-
resolution aerial imagery by intensive evaluation of a number of traditional and Deep …

In the field of computer vision, multi-object tracking (MOT) is a crucial task. It involves the
identification, tracking, and classification of multiple objects in videos, connecting their …

Multi-object tracking aims to estimate the complete trajectories of objects in a scene.
Distinguishing among objects efficiently and correctly in complex environments is a …

Recently, one-stage trackers that use a joint model to predict both detections and
appearance embeddings in one forward pass received much attention and achieved state-of …

Data association is a crucial component for any multiple object tracking (MOT) method that
follows the tracking-by-detection paradigm. To generate complete trajectories such methods …

Accurate data association is crucial in reducing confusion such as ID switches and
assignment errors in multi-object tracking (MOT). However existing advanced methods often …

The recent trend in multiple object tracking (MOT) is heading towards leveraging deep
learning to boost the tracking performance. In this paper, we propose a novel solution …

Data association-based multiple object tracking (MOT) involves multiple separated modules
processed or optimized differently, which results in complex method design and requires …