Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

Frequency Maps as Expert Instructions to Lessen Data Dependency on Real-time Traffic Light Recognition

Version 1 : Received: 4 August 2020 / Approved: 6 August 2020 / Online: 6 August 2020 (07:56:57 CEST)

How to cite: Macedo, H.; Almeida, T.; Matos, L.; Prado, B. Frequency Maps as Expert Instructions to Lessen Data Dependency on Real-time Traffic Light Recognition. Preprints 2020, 2020080138 (doi: 10.20944/preprints202008.0138.v1). Macedo, H.; Almeida, T.; Matos, L.; Prado, B. Frequency Maps as Expert Instructions to Lessen Data Dependency on Real-time Traffic Light Recognition. Preprints 2020, 2020080138 (doi: 10.20944/preprints202008.0138.v1).

Abstract

Research on Traffic Light Recognition (TLR) has grown in recent years, primarily driven by the growing interest in autonomous vehicles development. Machine Learning algorithms have been widely used to that purpose. Mainstream approaches, however, require large amount of data to properly work, and as a consequence, a lot of computational resources. In this paper we propose the use of Expert Instruction (IE) as a mechanism to reduce the amount of data required to provide accurate ML models for TLR. Given an image of the exterior scene taken from the inside of the vehicle, we stand the hypothesis that the picture of a traffic light is more likely to appear in the central and upper regions of the image. Frequency Maps of traffic light location were thus constructed to confirm this hypothesis. The frequency maps are the result of a manual effort of human experts in annotating each image with the coordinates of the region where the traffic light appears. Results show that EI increased the accuracy obtained by the classification algorithm in two different image datasets by at least 15%. Evaluation rates achieved by the inclusion of EI were also higher in further experiments, including traffic light detection followed by classification by the trained algorithm. The inclusion of EI in the PCANet achieved a precision of 83% and recall of 73% against 75.3% and 51.1%, respectively, of its counterpart. We finally presents a prototype of a TLR Device with that expert model embedded to assist drivers. The TLR uses a smartphone as a camera and processing unit. To show the feasibility of the apparatus, a dataset was obtained in real time usage and tested in an Adaptive Background Suppression Filter (AdaBSF) and Support Vector Machines (SVMs) algorithm to detect and recognize traffic lights. Results show precision of 100% and recall of 65%.

Subject Areas

Traffic Light Recognition (TLR); machine learning; Expert Instruction (EI); frequency maps; computer vision

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