Please use this identifier to cite or link to this item: http://hdl.handle.net/2440/107611
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Type: Journal article
Title: Fast detection of multiple objects in traffic scenes with a common detection framework
Author: Hu, Q.
Paisitkriangkrai, S.
Shen, C.
Van Den Hengel, A.
Porikli, F.
Citation: IEEE Transactions on Intelligent Transportation Systems, 2016; 17(4):1002-1014
Publisher: IEEE
Issue Date: 2016
ISSN: 1524-9050
1558-0016
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Responsibility: 
Qichang Hu, Sakrapee Paisitkriangkrai, Chunhua Shen, Anton van den Hengel, and Fatih Porikli
Abstract: Traffic scene perception (TSP) aims to extract accurate real-time on-road environment information, which involves three phases: detection of objects of interest, recognition of detected objects, and tracking of objects in motion. Since recognition and tracking often rely on the results from detection, the ability to detect objects of interest effectively plays a crucial role in TSP. In this paper, we focus on three important classes of objects: traffic signs, cars, and cyclists. We propose to detect all the three important objects in a single learning-based detection framework. The proposed framework consists of a dense feature extractor and detectors of three important classes. Once the dense features have been extracted, these features are shared with all detectors. The advantage of using one common framework is that the detection speed is much faster, since all dense features need only to be evaluated once in the testing phase. In contrast, most previous works have designed specific detectors using different features for each of these three classes. To enhance the feature robustness to noises and image deformations, we introduce spatially pooled features as a part of aggregated channel features. In order to further improve the generalization performance, we propose an object subcategorization method as a means of capturing the intraclass variation of objects. We experimentally demonstrate the effectiveness and efficiency of the proposed framework in three detection applications: traffic sign detection, car detection, and cyclist detection. The proposed framework achieves the competitive performance with state-of-the-art approaches on several benchmark data sets.
Keywords: Traffic scene perception; traffic sign detection; car detection; cyclist detection; object subcategorization
Rights: © 2015 IEEE. Personal use is permitted, but republication/redistribution requires IEEE permission. See http://www.ieee.org/publications_standards/publications/rights/index.html for more information.
RMID: 0030046937
DOI: 10.1109/TITS.2015.2496795
Appears in Collections:Computer Science publications

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