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Research Article | Open Access

Traffic signal detection and classification in street views using an attention model

TNList, Tsinghua University, Beijing 100084, China.
Department of Computer Science, University of Bath, United Kingdom.
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Abstract

Abstract  Detecting small objects is a challenging task. We focus on a special case: the detection and classification of traffic signals in street views. We present a novel framework that utilizes a visual attention model to make detection more efficient, without loss of accuracy, and which generalizes. The attention model is designed to generate a small set of candidate regions at a suitable scale so that small targets can be better located and classified. In order to evaluate our method in the context of traffic signal detection, we have built a traffic light benchmark with over 15,000 traffic light instances, based on Tencent street view panoramas. We have tested our method both on the dataset we have built and the Tsinghua–Tencent 100K (TT100K) traffic sign benchmark. Experiments show that our method has superior detection performance and is quicker than the general faster RCNN object detection framework on both datasets. It is competitive with state-of-the-art specialist traffic sign detectors on TT100K, but is an order of magnitude faster. To show generality, we tested it on the LISA dataset without tuning, and obtained an average precision in excess of 90%.

Keywords

Keywords  traffic light detectiontraffic light benchmarksmall object detectionCNN

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Computational Visual Media
Volume 4 Issue 3,
September 2018
Pages 253-266
DOI: 10.1007/s41095-018-0116-x
Cite this article:
Lu Y, Lu J, Zhang S, et al. Traffic signal detection and classification in street views using an attention model. Computational Visual Media, 2018, 4(3): 253-266. https://doi.org/10.1007/s41095-018-0116-x

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Revised: 09 March 2018
Accepted: 07 April 2018
Published: 04 August 2018
© The Author(s) 2018

This article is published with open access at Springerlink.com

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