Total views : 270
A Review: Techniques of Vehicle Detection in Fog
Objective: Here we are going to describe the technique to detect vehicle in foggy environment. Vehicle detection in foggy weather is important because poor visibility is the major reason of the accidents and collision of vehicle. LiDAR and cameras are often used for better performance. Method: The image which is received from the camera in foggy condition is totally distorted and blurred and it will not clear up to the desired level so that the vehicle in front is clearly visible to us, so in order to deblur our image and make it clear we will use Adaptive Gaussian Thresholding Technique. In this technique threshold value is the weighted sum of the neighborhood pixel values which will make our image clearer and clean as compared to the original image. In addition with camera we are using low cost LiDAR which consists of a laser and a camera both of these devices are combined to measure accurate distance up to 10 meter. This LiDAR will used to measure the distance from front vehicle and provide warning according to the measured distance. Finding: The coding of this system is completely based on the python which is faster and lightweight as compared to the MATLAB. And our LiDAR is also more accurate and fast as compared to the traditional LiDAR system its major plus point is that it is of low cost. The combination of LiDAR and camera make our system more powerful and efficient.
Adaptive Gaussian Thresholding, Computer Vision, LiDAR, Python.
- Zhang F, Clarke D, Knoll A. Vehicle detection based on LiDAR and camera fusion.17th International IEEE Conference on Intelligent Transportation Systems (ITSC); 2014. p. 1620–5.
- Takagi K, Morikawa K, Ogawa T, Saburi M. Road environment recognition using on-vehicle LiDAR. 2006 IEEE Intelligent Vehicles Symposium; 2006. p. 120–5.
- Dannheim C, Icking C, Mader M, Sallis P. Weather detection in vehicles by means of camera and LiDAR system. Sixth International Conference on Computational Intelligence, Communication Systems and Networks (CICSyN); 2014.p. 186–91.
- Hautiere N, Labayrade R, Aubert D. Detection of visibility conditions through use of onboard cameras. IEEE Proceedings of Intelligent Vehicles Symposium; 2005.p. 193–8.
- Huang L, Barth M. Tightly-coupled LiDAR and computer vision integration for vehicle detection. 2009 IEEE Intelligent Vehicles Symposium; 2009. p. 604–9.
- Zhao S, Farrell JA. 2D LiDAR aided INS for vehicle positioning in urban environment. IEEE International Conference on Control Applications (CCA); 2013. p. 376–81.
- Lu G, Tomizuka M. LiDAR sensing for vehicle lateral guidance: Algorithm and experimental study. IEEE/ASME Transactions on Mechatronics. 2006; 11(6):653–60.
- Image source available from: http://www.google.com/url?q=http%3A%2F%2Fforsys.cfr.washington.edu%2FJFSP06%2Flidar_technology.htm&sa=D&sntz=1& usg=AFQjCNFBKr9KxhjRkSD4UF9MGG-FF-nZOQ
- Image source available from: http://www.google.com/ url?q=http%3A%2F%2Fwww.flir.com%2Fcorporate%2Fdisplay%2F%3Fid%3D41523&sa=D&sntz=1&usg=AFQj CNEmgdZozD6s018o9VHEaskKyS92IA
- Image source :
- Zhang F, Clarke D, Knoll A. Vehicle detection based on LiDAR and camera fusion.17th International IEEE Conference on Intelligent Transportation Systems (ITSC); 2014. p. 1620–25.
- There are currently no refbacks.
This work is licensed under a Creative Commons Attribution 3.0 License.