This paper presents a new large scale multi-person tracking dataset. Our dataset is over an
order of magnitude larger than currently available high quality multi-object tracking datasets …

Recently there has been a significant amount of research on Multi-Camera People Tracking
(MCPT). MCPT presents more challenges compared to Multi-Object Single Camera Tracking …

Most multiple people tracking systems compute trajectories based on the tracking-by-
detection paradigm. Consequently, the performance depends to a large extent on the quality …

Multi-object tracking (MOT) is one of the significant directions of computer vision. Though
existing methods can solve simple tasks like pedestrian tracking well, some complex …

Multi-object tracking in video surveillance is subjected to illumination variation, blurring,
motion, and similarity variations during the identification process in real-world practice. The …

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 …

In this paper, we extend the discriminant correlation filter (DCF) based deep learning tracker
to multi-object tracking. For each object, we use an individual tracker to estimate the …

Joint detection and embedding (JDE) methods usually fuse the target motion information
and appearance information as the data association matrix, which could fail when the target …

In this work, we focus on improving online multi-object tracking (MOT). In particular, we
propose a novel region-based Siamese Multi-Object Tracking network, which we name …

Tracking-by-detection is one of the typical paradigms for multi-person tracking, due to the
availability of automatic pedestrian detectors. However, existing multi-person trackers are …