Survey on Detection Methods for Self-Driving Cars
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.
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.
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 )
Authors who submit papers with this journal agree to the following terms:
- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See The Effect of Open Access).
- By submitting the processing fee, it is understood that the author has agreed to our terms and conditions which may change from time to time without any notice.
- It should be clear for authors that the Editor In Chief is responsible for the final decision about the submitted papers; have the right to accept\reject any paper. The Editor In Chief will choose any option from the following to review the submitted papers:A. send the paper to two reviewers, if the results were negative by one reviewer and positive by the other one; then the editor may send the paper for third reviewer or he take immediately the final decision by accepting\rejecting the paper. The Editor In Chief will ask the selected reviewers to present the results within 7 working days, if they were unable to complete the review within the agreed period then the editor have the right to resend the papers for new reviewers using the same procedure. If the Editor In Chief was not able to find suitable reviewers for certain papers then he have the right to reject the paper.
- Author will take the responsibility what so ever if any copyright infringement or any other violation of any law is done by publishing the research work by the author
- Before publishing, author must check whether this journal is accepted by his employer, or any authority he intends to submit his research work. we will not be responsible in this matter.
- If at any time, due to any legal reason, if the journal stops accepting manuscripts or could not publish already accepted manuscripts, we will have the right to cancel all or any one of the manuscripts without any compensation or returning back any kind of processing cost.
- The cost covered in the publication fees is only for online publication of a single manuscript.