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Traffic Sign Detection and Vehicle Monitoring
Published in P. C. Thomas, Vishal John Mathai, Geevarghese Titus, Emerging Technologies for Sustainability, 2020
Abel Simon, Aiswarya Mary Babu, S. Ananthakrishnan
The real-time concentration of carbon monoxide is being updated in the firebase database using ML algorithms if the concentration in ppm goes above 20 which is the primary limit for automobiles CO emission a flag is set in the database to trigger and either send an email or SMS to the concerned. The whole CO concentration is readily available for evaluation. The traffic sign recognition has direct real-world applications such as driver assistance and safety, urban scene understanding, automated driving, or even sign monitoring for maintenance. It is a relatively constrained problem in the sense that signs are unique, rigid and intended to be clearly visible for drivers, and have little variability in appearance. A visual-based traffic sign recognition system can be implemented on the automobile with the aim of detecting and recognizing all emerging traffic signs. The same would be displayed to the driver with a heads-up display and alarm-triggering features if the driver refuses to follow the traffic signs.
AI for Advanced Driver Assistance Systems
Published in Josep Aulinas, Hanky Sjafrie, AI for Cars, 2021
Due to advances in ADAS, speed limit signs are far from being the only traffic signs that cars are able to recognize nowadays. AI has for example empowered vehicles to recognize no-entry/wrong-way, no-passing, yield and stop signs and even the color of traffic lights, from a distance of up to 150 meters [17]. The information is typically shown using pictograms displayed in the cluster-instrument, head-unit or head-up display. Traffic sign recognition is primarily done using cameras. Some studies have also proposed a sensor fusion of camera and lidar (light detection and ranging) to improve overall system accuracy [18], [19].
Review on Reliable Pattern Recognition with Machine Learning Techniques
Published in Fuzzy Information and Engineering, 2018
Devyani Bhamare, Poonam Suryawanshi
Zeng Y et al. have clarified a novel traffic sign recognition approach based on the examination on the impact that colour spaces have on the portrayal learning of the convolutional neural system. A DP-KELM was examined utilising a kernel-based extreme learning machine (KELM) classifier with profound perceptual features. Traffic sign recognition assumes a vital part in self-sufficient vehicles and in addition propelled driver help systems. Albeit different methods have been created, it was as yet troublesome for the state-of-the-art algorithms to get high recognition exactness with low computational expenses. Dissimilar to the past methodologies, the portrayal learning process in DP-KELM was executed in the perceptual Lab colour space. Based on the adapted profound perceptual feature, a kernel-based ELM classifier was prepared with high computational effectiveness and speculation execution. Through the analyses on the German traffic sign recognition benchmark, this method was exhibited to have higher exactness than a large portion of the state-of-the-art approaches. In particular, when contrasted and the hinge loss stochastic gradient descent method which has the highest exactness, this method can accomplish an equivalent recognition rate with significantly less computational expenses [17].
Traffic sign detection and positioning based on monocular camera
Published in Journal of the Chinese Institute of Engineers, 2019
Jen-Yu Han, Tsung-Hsien Juan, Tzu-Yi Chuang
Indeed, traffic sign recognition with a single or a stereo vision system is a well-established and well-studied area in the computer vision field. In contrast to existing studies, this study proposes a low-cost single camera system to locate and identify traffic signs in 3D space and further evaluate the states of the signs in terms of a least-squares consistency assessment. It should be noted that, for better accuracy, the 3D positions of the identified traffic signs are determined by the intersection of the image pair that yields the longest baseline within their sequential images.