We performed an electronic database search of published works from 2012 to mid-2021 that
focus on human gait studies and apply machine learning techniques. We identified six key …

The way people walk is a strong correlate of their identity. Several studies have shown that
both humans and machines can recognize individuals just by their gait, given that proper …

This paper considers person re-identification (re-id) in videos. We introduce a new video re-
id dataset, named M otion A nalysis and R e-identification S et (MARS), a video extension of …

This paper studies an approach to gait based human identification via similarity learning by
deep convolutional neural networks (CNNs). With a pretty small group of labeled multi-view …

Person re-identification is a fundamental task in automated video surveillance and has been
an area of intense research in the past few years. Given an image/video of a person taken …

Person re-identification (re-id) is a critical problem in video analytics applications such as
security and surveillance. The public release of several datasets and code for vision …

Existing person re-identification (re-id) benchmarks and algorithms mainly focus on
matching cropped pedestrian images between queries and candidates. However, it is …

This work proposes a method to characterize the appearance of individuals exploiting body
visual cues. The method is based on a symmetry-driven appearance-based descriptor and a …

Wide-scale use of visual surveillance in public spaces puts individual privacy at stake while
increasing resource consumption (energy, bandwidth, and computation). Neuromorphic …

We propose a novel Multi-Task Learning with Low Rank Attribute Embedding (MTL-LORAE)
framework for person re-identification. Re-identifications from multiple cameras are regarded …