Survey on Detection Methods for Self-Driving Cars

Myat Su Oo, Dr. May ` The` Yu

Abstract


Accurate vehicle detection or classification plays an important role for self-driving cars. Objects classification and detection can be used in various such as Robotics, Medical Diagnosis, Safety, Industrial Inspection and Automation, Human Computer Interface, Advanced Driver Assistance System and Information Retrieval. In this article, we investigated the methods of detection and classification in context images and videos. SIFT, HOG, SVM, CNN, faster RCNN and YOLO methods are reviewed to detect and recognize the objects. The paper aims to know the methods that detect the obstacles on the way to reduce the traffic accidents. We summarize the results, faster-RCNN is better than the other methods for real-time citing the advantages and disadvantages of existing methods.


Keywords


SIFT; HOG; SVM; CNN; faster RCNN; YOLO.

Full Text:

PDF

References


M. A. Manzoor and Y. Morgan, “Vehicle Make and Model classification system using bag of SIFT features”, in Computing and Communication Workshop and Conference (CCWC), 2017 IEEE 7th Annual, pp. 1–5, IEEE, 2017.

J.-W. Hsieh, L.-C. Chen, and D.-Y. Chen, “Symmetrical SURF and its applications to vehicle detection and vehicle make and model recognition,” IEEE Transactions on intelligent transportation systems, vol. 15, no. 1, pp. 6–20, 2014.

X. Li and X. Guo, “A HOG feature and SVM based method for forward vehicle detection with single camera,” in Intelligent Human-Machine Systems and Cybernetics (IHMSC), 2013 5th International Conference on, vol. 1, pp. 263–266, IEEE, 2013.

S. Sivaraman and M. M. Trivedi, “Looking at vehicles on the road: A survey of vision-based vehicle detection, tracking, and behavior analysis,” IEEE Transactions on Intelligent Transportation Systems, vol. 14, no. 4, pp. 1773–1795, 2013.

F. Han, Y. Shan, R. Cekander, H. S. Sawhney, and R. Kumar, “A two-stage approach to people and vehicle detection with HOG-based SVM,” in Performance Metrics for Intelligent Systems 2006 Workshop, pp. 133–140, 2006.

P. Negri, X. Clady, S. M. Hanif, and L. Prevost, “A cascade of boosted generative and discriminative classifiers for vehicle detection,” EURASIP Journal on Advances in Signal Processing, vol. 2008, p. 136, 2008.

Sundaresh Ram, Jeffrey J. Rodriguez, “Vehicle Detection In Aerial Images Using Multiscale Structure Enhancement And Symmetry” ,Icip(Ieee),Pp 3817-3821,2016.

Z. Dong, Y. Wu, M. Pei, and Y. Jia, “Vehicle type classification using a semi supervised convolutional neural network,” IEEE transactions on intelligent transportation systems, vol. 16, no. 4, pp. 2247–2256, 2015.

Z. Dong, M. Pei, Y. He, T. Liu, Y. Dong, and Y. Jia, “Vehicle Type Classification Using Unsupervised Convolutional Neural Network,” in 2014 22nd International Conference on Pattern Recognition, pp. 172–177, Aug 2014.

C. M. Bautista, C. A. Dy, M. I. Mañalac, R. A. Orbe, and M. Cordel, “Convolutional neural network for vehicle detection in low resolution traffic videos,” in Region 10 Symposium (TENSYMP), 2016 IEEE, pp. 277–281, IEEE, 2016.

H. Huttunen, F. S. Yancheshmeh, and K. Chen, “Car type recognition with deep neural networks,” in Intelligent Vehicles Symposium (IV), 2016 IEEE, pp. 1115– 1120, IEEE, 2016.

Y. Zhou, H. Nejati, T.-T. Do, N.-M. Cheung, and L. Cheah, “Image-based vehicle analysis using deep neural network: A systematic study,” in Digital Signal Processing (DSP), 2016 IEEE International Conference on, pp. 276–280, IEEE, 2016.

B. Huval, T. Wang, S. Tandon, J. Kiske, W. Song, J. Pazhayampallil, M. Andriluka, R. Cheng-Yue, F. Mujica, A. Coates, et al. An empirical evaluation of deep learning on highway driving. arXiv preprint arXiv:1504.01716, 2015.

https://arxiv.org/pdf/1311.2524.pdf

https://arxiv.org/pdf/1504.08083.pdf

https://arxiv.org/pdf/1506.01497.pdf

https://arxiv.org/pdf/1506.02640v5.pdf

http://cs231n.stanford.edu/slides/2017/cs231n_2017_lecture11.pdf

B. Girshick, J. Donahue, T. Darrell, and J. Malik, “Rich feature hierarchies for accurate object detection and semantic segmentation,” CoRR, vol. abs/1311.2524, 2013.

Aysxegu Ucxar, Yakup Demir and Cu¨neyt Gu¨zelisx “Object recognition and detection with deep learning for autonomous driving applications” Transactions of the Society for Modeling and Simulation International 2017, Vol. 93(9) 759–769

A. Krizhevsky, I. Sutskever, and G. E. Hinton, “Imagenet classification with deep convolutional neural networks,” in Advances in neural information processing systems, pp. 1097–1105, 2012.

K. Simonyan and A. Zisserman, “Very deep convolutional networks for large-scale image recognition,” arXiv preprint arXiv:1409.1556, 2014.

J. Wang, Y. Yang, J. Mao, Z. Huang, C. Huang, and W. Xu, “Cnn-rnn: A unified framework for multi-label image classification,” in Computer Vision and Pattern Recognition (CVPR), 2016 IEEE Conference on, pp. 2285–2294, IEEE, 2016.

R. Girshick, J. Donahue, T. Darrell, and J. Malik, “Region-based convolutional networks for accurate object detection and segmentation,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 38, no.1, pp.142–158, Jan.2016.

Amir Mukhtar, Likun Xia, Tong Boon Tang “Vehicle Detection Techniques for Collision Avoidance Systems: A Review” IEEE Transactions on Intelligent Transportation Systems Volume: 16 ,Issue: 5 , Oct. 2015 )


Refbacks

  • There are currently no refbacks.


 

 
  

 

  


About IJC | Privacy PolicyTerms & Conditions | Contact Us | DisclaimerFAQs 

IJC is published by (GSSRR).