NDLI: Evaluation of Boosting-SVM and SRM-SVM cascade classifiers in laser and vision-based pedestrian detection

Content Provider	IEEE Xplore Digital Library
Author	Ludwig, O. Premebida, C. Nunes, U. Araujo, R.
Copyright Year	2011
Description	Author affiliation: Department of Electrical and Computer Engineering, Institute of Systems and Robotics, University of Coimbra, Portugal (Ludwig, O.; Premebida, C.; Nunes, U.; Araujo, R.)
Abstract	Pedestrian detection systems constitute an important field of research and development in computer vision, specially when applied in protection/safety systems in urban scenarios due to their direct impact in the society, specifically in terms of traffic casualties. In order to face such challenge, this work exploits some developments on statistical machine learning theory, in particular structural risk minimization (SRM) in a cascade ensemble. Namely, the ensemble applies the principle of SRM on a set of linear support vector machines (SVM). The linear SVM complexity, in the Vapnik sense, is controlled by choosing the dimension of the feature space in each cascade stage. To support experimental analysis, a multi-sensor dataset constituted by data from a LIDAR, a monocular camera, an IMU, encoder and a DGPS is introduced in this paper. The dataset, named Laser and Image Pedestrian Detection (LIPD) dataset, was collected in an urban environment, at day light conditions, using an electrical vehicle driven at low speed. Labeled pedestrians and non-pedestrians samples are also available for benchmarking purpose. The cascade of SVMs, trained with image-based features (HOG and COV descriptors), is used to detect pedestrian evidences on regions of interest (ROI) generated by a LIDAR-based processing system. Finally, the paper presents experimental results comparing the performance of a Boosting-SVM cascade and the proposed SRM-SVM cascade classifiers, in terms of detection errors.
Starting Page	1574
Ending Page	1579
File Size	950926
Page Count	6
File Format	PDF
ISBN	9781457721984
ISSN	21530009
e-ISBN	9781457721977
DOI	10.1109/ITSC.2011.6082909
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2011-10-05
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Training Support vector machines Feature extraction Robot sensing systems Laser radar Detectors Complexity theory
Content Type	Text
Resource Type	Article

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1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
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3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Mr. Siddhartha Mukherjee siddhartha@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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