The human pose estimation is a significant issue that has been taken into consideration in
the computer vision network for recent decades. It is a vital advance toward understanding …

Abstract Multiple Object Tracking (MOT) has emerged as a hot issue in the field of computer
vision recently. MOT has academic and commercial potential in urban public security …

Exploring robust and efficient association methods has always been an important issue in
multi-object tracking (MOT). Although existing tracking methods have achieved impressive …

Multi-object tracking (MOT) is a highly valued and challenging research topic in computer
vision. To achieve more robust tracking performance, recently published MOT methods tend …

Without manually annotated identities, unsupervised multi-object trackers are inferior to
learning reliable feature embeddings. It causes the similarity-based inter-frame association …

Object tracking (OT) aims to estimate the positions of target objects in a video sequence.
Depending on whether the initial states of target objects are specified by provided …

Multi-object tracking (MOT) endeavors to precisely estimate the positions and identities of
multiple objects over time. The prevailing approach, tracking-by-detection (TbD), first detects …

Multi-object tracking (MOT) aims to associate target objects across video frames in order to
obtain entire moving trajectories. With the advancement of deep neural networks and the …

Multi-modal (ie, visible, near-infrared, and thermal-infrared) vehicle re-identification has
good potential to search vehicles of interest in low illumination. However, due to the fact that …

The ability to track multiple objects is crucial for roadside units to provide high-precision,
trajectory-based traffic data, especially for connected vehicles that require complementary …