NDLI: Real-time pedestrian detection with deformable part models

Content Provider	IEEE Xplore Digital Library
Author	Hyunggi Cho Rybski, P.E. Bar-Hillel, A. Wende Zhang
Copyright Year	2012
Description	Author affiliation: ECE and the Robotics Institute, respectively, Carnegie Mellon University, 5000, Forbes Ave., Pittsburgh, PA 15213, USA (Hyunggi Cho; Rybski, P.E.) \|\| Electrical and Controls Integration Lab, General Motors R&D, 30500, Mound Rd, Warren, MI 48092, USA (Wende Zhang) \|\| Advanced Technical Center, General Motors, Hamanofim 11, Herzliya, Israel (Bar-Hillel, A.)
Abstract	We describe a real-time pedestrian detection system intended for use in automotive applications. Our system demonstrates superior detection performance when compared to many state-of-the-art detectors and is able to run at a speed of 14 fps on an Intel Core i7 computer when applied to 640×480 images. Our approach uses an analysis of geometric constraints to efficiently search feature pyramids and increases detection accuracy by using a multiresolution representation of a pedestrian model to detect small pixel-sized pedestrians normally missed by a single representation approach. We have evaluated our system on the Caltech Pedestrian benchmark which is currently the largest publicly available pedestrian dataset at the time of this publication. Our system shows a detection rate of 61% with 1 false positive per image (FPPI) whereas recent other state-of-the-art detectors show a detection rate of 50% ∼ 61% under the ‘reasonable’ test scenario (explained later). Furthermore, we also demonstrate the practicality of our system by conducting a series of use case experiments on selected videos of Caltech dataset.
Starting Page	1035
Ending Page	1042
File Size	2027266
Page Count	8
File Format	PDF
ISBN	9781467321198
ISSN	19310587
e-ISBN	9781467321181
DOI	10.1109/IVS.2012.6232264
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2012-06-03
Publisher Place	Spain
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Detectors Computational modeling Real time systems Deformable models Feature extraction Training Vehicles
Content Type	Text
Resource Type	Article

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