Visual Detection of Moving Vehicles Ahead Based on the Characteristics

2011 ◽  
Vol 103 ◽  
pp. 165-169
Author(s):  
Hou Yun Yu ◽  
Wei Gong Zhang

Machine vision perception technology is widely used in the vehicle’s active safety system. It provides more immediate and correct information of road and vehicles around, in which inspection of moving vehicle ahead is one of the important items. A method of inspection fused of detection of the shadow under the vehicle and symmetry of the vehicle’s tail is presented in this paper. At first, a region of interest is selected according to the lane lines. Then, the shadow can be detected with grayscale histogram in the region of interest and a suspected area of vehicle is obtained by expanding the shadow with empirical proportion. At last, the vehicle ahead is further affirmed by calculating the symmetry of such characteristic at its tail as grayscale value, taillight and the edges. Experimental results prove that this method can well solve the actual problems of vehicle detection.

2013 ◽  
Vol 40 (17) ◽  
pp. 6714
Author(s):  
Vicente Milanés ◽  
David F. Llorca ◽  
Jorge Villagrá ◽  
Joshue Pérez ◽  
Ignacio Parra ◽  
...  

2021 ◽  
Vol 150 ◽  
pp. 105857
Author(s):  
Zhengping Tan ◽  
Yaoyue Che ◽  
Lingyun Xiao ◽  
Wenhao Hu ◽  
Pingfei Li ◽  
...  

2016 ◽  
Vol 2016 ◽  
pp. 1-13 ◽  
Author(s):  
Chi Guo ◽  
Guangyi Cao ◽  
Jieru Zeng ◽  
Jinsong Cui ◽  
Rong Peng

Perceiving the location of dangerous moving vehicles and broadcasting this information to vehicles nearby are essential to achieve active safety in the Internet of Vehicles (IOV). To address this issue, we implement a real-time high-precision lane-level danger region service for moving vehicles. A traditional service depends on static geofencing and fails to deal with dynamic vehicles. To overcome this defect, we devised a new type of IOV service that manages to track dangerous moving vehicles in real time and recognize their danger regions quickly and accurately. Next, we designed algorithms to distinguish the vehicles in danger regions and broadcast the information to these vehicles. Our system can simultaneously manipulate a mass of danger regions for various dangerous vehicles and broadcast this information to surrounding vehicles at a large scale. This new system was tested in Shanghai, Guangzhou, Wuhan, and other cities; the data analysis is presented in this paper as well.


2017 ◽  
Vol 18 (2) ◽  
pp. 377-387 ◽  
Author(s):  
Vicent Girbes ◽  
Leopoldo Armesto ◽  
Juan Dols ◽  
Josep Tornero

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