Traffic Symbol Detection and Recognition System

Hussain, K. Md. Zakir; Kattigenahally, Komal Nagaraj; Nikitha, S.; Jena, Pabitra Priyadarshini; Harshalatha, Y.

doi:10.1007/978-981-16-1342-5_70

Cited by 1 publication

(1 citation statement)

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“…In the literature, there are many studies [ 66 , 67 , 68 , 69 ] focusing on single frame TSR, and very few studies [ 64 , 65 ] that process multiple frames. According to our knowledge based on the literature review, there is no study available that considers the sliding windows approach for traffic sign recognition.…”

Section: Background On Traffic Sign Recognitionmentioning

confidence: 99%

Towards Enhancing Traffic Sign Recognition through Sliding Windows

Atif¹,

Zoppi²,

Gharib

et al. 2022

Sensors

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Automatic Traffic Sign Detection and Recognition (TSDR) provides drivers with critical information on traffic signs, and it constitutes an enabling condition for autonomous driving. Misclassifying even a single sign may constitute a severe hazard, which negatively impacts the environment, infrastructures, and human lives. Therefore, a reliable TSDR mechanism is essential to attain a safe circulation of road vehicles. Traffic Sign Recognition (TSR) techniques that use Machine Learning (ML) algorithms have been proposed, but no agreement on a preferred ML algorithm nor perfect classification capabilities were always achieved by any existing solutions. Consequently, our study employs ML-based classifiers to build a TSR system that analyzes a sliding window of frames sampled by sensors on a vehicle. Such TSR processes the most recent frame and past frames sampled by sensors through (i) Long Short-Term Memory (LSTM) networks and (ii) Stacking Meta-Learners, which allow for efficiently combining base-learning classification episodes into a unified and improved meta-level classification. Experimental results by using publicly available datasets show that Stacking Meta-Learners dramatically reduce misclassifications of signs and achieved perfect classification on all three considered datasets. This shows the potential of our novel approach based on sliding windows to be used as an efficient solution for TSR.

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