Research on Vehicle Detection Algorithm of Driver Assistance System Based on Vision

Yuan, Chenyang; Huo, Chunbao; Tong, Zhibo; Men, Guangwen; Wang, Yan

doi:10.1109/ccdc.2019.8832946

Cited by 3 publications

(1 citation statement)

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“…Video surveillance systems have been widely used in this type of application, mainly because these systems are flexible and versatile, allowing the identification of movements and paths traced by vehicles [ 15 ]. Many papers describe solutions based on vision techniques and the AdaBoost learning algorithm due to their ability to detect and track vehicles in highly changing environments [ 16 , 17 , 18 ]. In [ 19 ], outdoor security cameras were integrated with a neural network classifier and the Mobilenet V1 SSD object detection model to detect and track vehicles, while in [ 20 ] a random forest (RF)-based method was proposed to detect vehicles under non-optimal lighting conditions.…”

Section: Related Workmentioning

confidence: 99%

Analysis of Machine Learning Techniques Applied to Sensory Detection of Vehicles in Intelligent Crosswalks

Domínguez

Al-Tam

Sanguino

et al. 2020

Sensors

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Improving road safety through artificial intelligence-based systems is now crucial turning smart cities into a reality. Under this highly relevant and extensive heading, an approach is proposed to improve vehicle detection in smart crosswalks using machine learning models. Contrarily to classic fuzzy classifiers, machine learning models do not require the readjustment of labels that depend on the location of the system and the road conditions. Several machine learning models were trained and tested using real traffic data taken from urban scenarios in both Portugal and Spain. These include random forest, time-series forecasting, multi-layer perceptron, support vector machine, and logistic regression models. A deep reinforcement learning agent, based on a state-of-the-art double-deep recurrent Q-network, is also designed and compared with the machine learning models just mentioned. Results show that the machine learning models can efficiently replace the classic fuzzy classifier.

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