@article{ref1,
title="Video-based pedestrian re-identification by adaptive spatio-temporal appearance model",
journal="IEEE transactions on image processing",
year="2017",
author="Zhang, Wei and Ma, Bingpeng and Liu, Kan and Huang, Rui",
volume="26",
number="4",
pages="2042-2054",
abstract="Pedestrian re-identification is a difficult problem due to the large variations in a person's appearance caused by different poses and viewpoints, illumination changes, and occlusions. Spatial alignment is commonly used to address these issues by treating the appearance of different body parts independently. However, a body part can also appear differently during different phases of an action. In this paper we consider the temporal alignment problem, in addition to the spatial one, and propose a new approach that takes the video of a walking person as input and builds a spatio-temporal appearance representation for pedestrian re-identification. Particularly, given a video sequence we exploit the periodicity exhibited by a walking person to generate a spatio-temporal body-action model, which consists of a series of body-action units corresponding to certain action primitives of certain body parts. Fisher vectors are learned and extracted from individual body-action units and concatenated into the final representation of the walking person. Unlike previous spatio-temporal features that only take into account local dynamic appearance information, our representation aligns the spatio-temporal appearance of a pedestrian globally. Extensive experiments on public datasets show the effectiveness of our approach compared with the state of the art.<p /> <p>Language: en</p>",
language="en",
issn="1057-7149",
doi="10.1109/TIP.2017.2672440",
url="http://dx.doi.org/10.1109/TIP.2017.2672440"
}