Abstract Person Re-Identification (Re-ID) is a critical aspect of visual surveillance systems,
which aims to automatically recognize and locate individuals across a multi-camera network …

For clustering-guided fully unsupervised person reidentification (re-ID) methods, the quality
of pseudo labels generated by clustering directly decides the model performance. In order to …

Person re-identification (Re-ID) has achieved great improvement with deep learning and a
large amount of labelled training data. However, it remains a challenging task for adapting a …

The fundamental difficulty in person re-identification (ReID) lies in learning the
correspondence among individual cameras. It strongly demands costly inter-camera …

Gait is one of the most promising biometrics to identify individuals at a long distance.
Although most previous methods have focused on recognizing the silhouettes, several end …

Recently, some methods have focused on learning local relation among parts of pedestrian
images for person re-identification (Re-ID), as it offers powerful representation capabilities …

Despite the impressive performance under the single-domain setup, current fully-supervised
models for person re-identification (re-ID) degrade significantly when deployed to an unseen …

The trained person re-identification systems fundamentally need to be deployed on different
target environments. Learning the cross-domain model has great potential for the scalability …

Construction sites are highly hazardous due to the dynamic interaction between workers
and moving equipment, with high fatality rates caused by collision and falling from height …

Recently, person re-identification (ReID) has vastly benefited from the surging waves of data-
driven methods. However, these methods are still not reliable enough for real-world …