Multi-object tracking is a vital component in various robotics and computer vision
applications. However, existing multi-object tracking techniques trade off computation …

Deep learning methods have led to remarkable progress in multiple object tracking (MOT).
However, when tracking in crowded scenes, existing methods still suffer from both …

Standardized benchmarks are crucial for the majority of computer vision applications.
Although leaderboards and ranking tables should not be over-claimed, benchmarks often …

Standardized benchmarks are crucial for the majority of computer vision applications.
Although leaderboards and ranking tables should not be over-claimed, benchmarks often …

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 …

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 paper, we propose a collaborative deep reinforcement learning (C-DRL) method for
multi-object tracking. Most existing multi-object tracking methods employ the tracking-by …

Modern multiple object tracking (MOT) systems usually follow the tracking-by-detection
paradigm. It has 1) a detection model for target localization and 2) an appearance …

Multi-object Tracking is an important issue that has been widely investigated in computer
vision. However, in practical applications, moving targets are often occluded due to complex …

Existing multi-object trackers mainly apply the tracking-by-detection (TBD) paradigm and
have achieved remarkable success. However, the mainstream methods execute their …