DMCA
Pedestrian Detection Via Classification on Riemannian Manifolds (2008)
| Citations: | 140 - 3 self |
BibTeX
@MISC{Tuzel08pedestriandetection,
author = {Oncel Tuzel and Fatih Porikli and Peter Meer},
title = {Pedestrian Detection Via Classification on Riemannian Manifolds},
year = {2008}
}
Share
 |
 |
 |
 |
||
OpenURL
Abstract
Detecting different categories of objects in image and video content is one of the fundamental tasks in computer vision research. The success of many applications such as visual surveillance, image retrieval, robotics, autonomous vehicles, and smart cameras are conditioned on the accuracy of the detection process. Two main processing steps can be distinguished in a typical object detection algorithm. The first task is feature extraction, in which the most informative object descriptors regarding the detection process are obtained from the visual content. The second task is detection, in which the obtained object descriptors are utilized in a classification framework to detect the objects of interest. The feature extraction methods can be further categorized into two groups based on the representation. The first group of methods is the sparse representations, where a set of representative local regions is obtained as the result of an interest point detection algorithm. Reliable interest points should encapsulate valuable information about the local image content and remain stable under changes, such as in viewpoint and/or illumination. There exists an extensive literature on interest point detectors, and [14],[18],[21],[25], and [27] are only a few of the most commonly used methods that satisfy consistency over a large range of operating conditions.
Keyphrases
riemannian manifold pedestrian detection via classification detection process smart camera large range many application feature extraction classification framework informative object descriptor valuable information representative local region autonomous vehicle visual surveillance first group local image content reliable interest point different category image retrieval feature extraction method interest point detection algorithm object descriptor fundamental task video content operating condition satisfy consistency visual content typical object detection algorithm first task second task sparse representation extensive literature computer vision research interest point detector
