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 …

Multi-object tracking has been a key research subject in many computer vision applications.
We propose a novel approach based on multi-agent deep reinforcement learning (MADRL) …

Recent progress in multiple object tracking (MOT) has shown that a robust similarity score is
a key to the success of trackers. A good similarity score is expected to reflect multiple cues …

Deep learning has been proved effective in multiple object tracking, which confronts the
difficulties of frequent occlusions, confusing appearance, in‐and‐out objects, and lack of …

Multi-object tracking (MOT) has made great progress in recent years, but there are still some
problems. Most MOT algorithms follow tracking-by-detection framework, which separates …

The recent trend in vision-based multi-object tracking (MOT) is heading towards leveraging
the representational power of deep learning to jointly learn to detect and track objects …

Traditional multiple object tracking methods divide the task into two parts: affinity learning
and data association. The separation of the task requires to define a hand-crafted training …

Recent progress in multi-object tracking (MOT) has shown great significance of a robust
scoring mechanism for potential tracks. However, the lack of available data in MOT makes it …

The recent trend in multiple object tracking (MOT) is heading towards leveraging deep
learning to boost the tracking performance. However, it is not trivial to solve the data …

Abstract We propose Quadruplet Convolutional Neural Networks (Quad-CNN) for multi-
object tracking, which learn to associate object detections across frames using quadruplet …