In recent years, several methods and datasets have been proposed to push the performance
of pedestrian detection in crowded scenarios. In this study, three crowd-specific detectors …

One of the biggest challenges in multi-pedestrian tracking arises in crowds, where missing
detections can lead to wrong track-detection assignments, especially under heavy …

The existing pedestrian tracking applications are challenging to balance real-time
performance and accuracy. We propose a detection–tracking–correction strategy based on …

Multiple pedestrian tracking in the first-person perspective is a challenging problem,
obstacles of which are mainly caused by camera moving, frequent occlusions, and collision …

Multi-pedestrian trackers perform well when targets are clearly visible making the
association task quite easy. However, when heavy occlusions are present, a mechanism to …

Multiple pedestrian tracking (MPT) has gained significant attention due to its huge potential
in a commercial application. It aims to predict multiple pedestrian trajectories and maintain …

Numerous object-tracking and multiple-person-tracking algorithms have been developed in
the field of computer vision, but few trackers can properly address the issue of when a …

Multi-object tracking is a vital component in various robotics and computer vision
applications. However, existing multi-object tracking techniques trade off computation …

Multiple pedestrian tracking is a critical yet challenging to understand human behavior.
Although existing tracking methods have achieved great advances through adopting the …

We present a pedestrian tracking algorithm, DensePeds, that tracks individuals in highly
dense crowds (> 2 pedestrians per square meter). Our approach is designed for videos …