Object tracking has gained importance in various applications especially in traffic
monitoring, surveillance and security, people tracking, etc. Previous methods of multiobject …

Most state-of-the-art multiple-object tracking (MOT) methods adopt the tracking-by-detection
(TBD) paradigm, which is a two-step procedure including the detection module and the …

Multiple object tracking is challenging due to the complex spatiotemporal relationship and
the occlusion of different targets. Most existing methods use separate neural networks to …

Most of the existing Multi-Object Tracking (MOT) approaches follow the Tracking-by-
Detection and Data Association paradigm, in which objects are firstly detected and then …

The stable continuation of trajectories among different targets has always been the key to
the tracking performance of multi-object tracking (MOT) tasks. If features of the target are …

Online multiple object tracking (MOT) is highly challenging when multiple objects have
similar appearance or under long occlusion. In this letter, we propose a semi-online MOT …

Detection and learning based appearance feature play the central role in data association
based multiple object tracking (MOT), but most recent MOT works usually ignore them and …

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 …

Multi-object tracking (MOT) has wide applications in the fields of video analysis and signal
processing. A major challenge in MOT is how to associate the noisy detections into long and …

Recently, with the development of deep-learning, the performance of multi-object tracking
algorithms based on deep neural networks has been greatly improved. However, most …