Near-Infrared-Based Nighttime Pedestrian Detection Using Grouped Part Models
Journal
IEEE Transactions on Intelligent Transportation Systems
Journal Volume
16
Journal Issue
4
Pages
1929-1940
Date Issued
2015
Author(s)
Abstract
Pedestrian detection is an important issue in the field of intelligent transportation systems. As a pedestrian is not an apparent object at nighttime, it brings about critical difficulties in effectively detecting a pedestrian for a driving assistant vision system. While using an infrared projector to enhance the illumination contrast, objects in a nighttime environment might reflect the infrared projected by the emitted spotlight. In some cases, however, the clothes on a pedestrian might absorb most of the infrared, thus causing the pedestrian to be partially invisible. To deal with this problem, a nighttime part-based pedestrian detection method is proposed. It divides a pedestrian into parts for a moving vehicle with a camera and a near-infrared lighting projector. Due to a high computation load, selecting effective parts becomes imperative. By analyzing the spatial relationship between every pair of parts, the confidence of the detected parts can be enhanced even when some parts are occluded. At the last stage of this system, the pedestrian detection result is refined by a block-based segmentation method. The system is verified by experiments, and the appealing results are demonstrated. © 2000-2011 IEEE.
Subjects
Geometric information; histogram of oriented gradient (HOG); near infrared (NIR); nighttime; part based; pedestrian detection; spatial relationship
SDGs
Other Subjects
Amphibious vehicles; Computer vision; Intelligent systems; Object detection; Geometric information; Histogram of oriented gradients (HOG); Near infra red; nighttime; Part based; Pedestrian detection; Spatial relationships; Infrared devices
Type
journal article