Joint human detection from static and mobile cameras

Miseikis, Justinas and Borges, Paulo Vinicius Koerich (2015) Joint human detection from static and mobile cameras. IEEE Transactions on Intelligent Transportation Systems, 16 2: 1018-1029. doi:10.1109/TITS.2014.2350979

Author Miseikis, Justinas
Borges, Paulo Vinicius Koerich
Title Joint human detection from static and mobile cameras
Journal name IEEE Transactions on Intelligent Transportation Systems   Check publisher's open access policy
ISSN 1524-9050
Publication date 2015-04
Year available 2014
Sub-type Article (original research)
DOI 10.1109/TITS.2014.2350979
Open Access Status
Volume 16
Issue 2
Start page 1018
End page 1029
Total pages 12
Place of publication Piscataway, NJ United States
Publisher Institute of Electrical and Electronics Engineers
Collection year 2016
Language eng
Abstract Efficient pedestrian detection is a key aspect of many intelligent vehicles. In this context, vision-based detection has increased in popularity. Algorithms proposed often consider that the camera is mobile (on board a vehicle) or static (mounted on infrastructure). In contrast, we consider a pedestrian detection approach that uses information from mobile and static cameras jointly. Assuming that the vehicle (on which the mobile camera is mounted) contains some sort of localization capability, combining information from the mobile camera with the static camera yields significantly improved detection rates. These sources are fairly independent, with substantially different illumination and view-angle perspectives, bringing more statistical diversity than a multicamera network observing an area of interest, for example. The proposed method finds applicability in industrial environments, where industrial vehicle localization is becoming increasingly popular. We implemented and tested the system on an automated industrial vehicle, considering both manned and autonomous operations. We present a thorough discussion on practical issues (resolution, lighting, subject pose, etc.) related to human detection in the scenario considered. Experiments illustrate the improved results of the joint detection compared with traditional independent static and mobile detection approaches.
Keyword Autonomous vehicles
Information fusion
Pedestrian detection
Q-Index Code C1
Q-Index Status Confirmed Code
Institutional Status UQ

Document type: Journal Article
Sub-type: Article (original research)
Collections: Official 2016 Collection
School of Information Technology and Electrical Engineering Publications
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