Acoustic Signal Based Automatic Vehicle Detection System

2012 ◽  
Vol 2 (4) ◽  
pp. 88-89
Author(s):  
Sai Sandeep.k Sai Sandeep.k ◽  
◽  
P. Vijay Kumar
Keyword(s):  
Acoustic Signal ◽  
Detection System ◽  
Vehicle Detection

scholarly journals Lightweight PVIDNet: A Priority Vehicles Detection Network Model Based on Deep Learning for Intelligent Traffic Lights

Sensors ◽  
2020 ◽  
Vol 20 (21) ◽  
pp. 6218
Author(s):  
Rodrigo Carvalho Barbosa ◽  
Muhammad Shoaib Ayub ◽  
Renata Lopes Rosa ◽  
Demóstenes Zegarra Rodríguez ◽  
Lunchakorn Wuttisittikulkij
Keyword(s):  
Deep Learning ◽  
Traffic Control ◽  
Detection System ◽  
Lightweight Design ◽  
Vehicle Detection ◽  
Activation Function ◽  
Urban Traffic ◽  
Vehicle Classification ◽  
Traffic Light ◽  
Traffic Lights

Minimizing human intervention in engines, such as traffic lights, through automatic applications and sensors has been the focus of many studies. Thus, Deep Learning (DL) algorithms have been studied for traffic signs and vehicle identification in an urban traffic context. However, there is a lack of priority vehicle classification algorithms with high accuracy, fast processing, and a lightweight solution. For filling those gaps, a vehicle detection system is proposed, which is integrated with an intelligent traffic light. Thus, this work proposes (1) a novel vehicle detection model named Priority Vehicle Image Detection Network (PVIDNet), based on YOLOV3, (2) a lightweight design strategy for the PVIDNet model using an activation function to decrease the execution time of the proposed model, (3) a traffic control algorithm based on the Brazilian Traffic Code, and (4) a database containing Brazilian vehicle images. The effectiveness of the proposed solutions were evaluated using the Simulation of Urban MObility (SUMO) tool. Results show that PVIDNet reached an accuracy higher than 0.95, and the waiting time of priority vehicles was reduced by up to 50%, demonstrating the effectiveness of the proposed solution.

scholarly journals Perception advances in outdoor vehicle detection for automatic cruise control

Robotica ◽  
2009 ◽  
Vol 28 (5) ◽  
pp. 765-779 ◽  
Author(s):  
S. Álvarez ◽  
M. Á. Sotelo ◽  
M. Ocaña ◽  
D. F. Llorca ◽  
I. Parra ◽  
...  
Keyword(s):  
Intelligent Transportation Systems ◽  
Detection System ◽  
Low Cost ◽  
Vehicle Detection ◽  
Basic Feature ◽  
Transportation Systems ◽  
Support Vector ◽  
Cruise Control ◽  
Vehicle To Vehicle ◽  
Time Gap

SUMMARYThis paper describes a vehicle detection system based on support vector machine (SVM) and monocular vision. The final goal is to provide vehicle-to-vehicle time gap for automatic cruise control (ACC) applications in the framework of intelligent transportation systems (ITS). The challenge is to use a single camera as input, in order to achieve a low cost final system that meets the requirements needed to undertake serial production in automotive industry. The basic feature of the detected objects are first located in the image using vision and then combined with a SVM-based classifier. An intelligent learning approach is proposed in order to better deal with objects variability, illumination conditions, partial occlusions and rotations. A large database containing thousands of object examples extracted from real road scenes has been created for learning purposes. The classifier is trained using SVM in order to be able to classify vehicles, including trucks. In addition, the vehicle detection system described in this paper provides early detection of passing cars and assigns lane to target vehicles. In the paper, we present and discuss the results achieved up to date in real traffic conditions.

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