Multiple object tracking (MOT), as a typical application scenario of computer vision, has
attracted significant attention from both academic and industrial communities. With its rapid …

Enhanced animal welfare has emerged as a pivotal element in contemporary precision
animal husbandry, with bovine monitoring constituting a significant facet of precision …

Despite extensive exploration of more powerful multi-object tracking (MOT) frameworks, the
impact of frequent occlusion has remained a formidable challenge. In this work, we present …

Self-supervised multi-object trackers have tremendous potential as they enable learning
from raw domain-specific data. However, their re-identification accuracy still falls short …

Collecting training data for deep models from the Internet is a common data acquisition
approach. However, there are challenges in using these data directly, as they often contain …

Traffic closed-circuit television (CCTV) devices can be used to detect and track objects on
roads by designing and applying artificial intelligence and deep learning models. However …

Many Siamese tracking algorithms attempt to enhance the target representation through
target aware. However, the tracking results are often disturbed by the target-like background …

Tracking multiple faces is a difficult problem, as there may be partially occluded or lateral
faces. In multiple face tracking, association is typically based on (biometric) face features …

Recently, the bottleneck of multi-object tracking is shifting from detection performance to
association performance. However, research on association algorithms requires a large …

Multi-object tracking is a research hotspot in intelligent driving scenarios, and the trajectory
association of tracked objects is one of the hotspots that needs to be solved urgently. The …