Abstract
In recent years, autonomous driving has inexorably progressed from the domain of science fiction to reality. For a self-driving car, it is of utmost importance that it knows its surroundings. Several sensors like RADARs, LiDARs, and Cameras have been primarily used to sense the environment and make a judgment on the next course of action. Object detection is of a great significance in Autonomous Driving wherein the self-driving car needs to identify the objects around it and must take necessary actions to avoid a collision. Several perception-based methods like classical Computer Vision techniques and Convolutional Neural Networks (CNN) exist today which detects and classifies an object. This paper discusses an object detection technique based on Stereo Vision. One challenge in this process though is to eliminate regions of the image which are insignificant for the detection, like unoccupied road and buildings far ahead. This paper proposes a method to first get rid of such regions using V-Disparity and then detect objects using 3D density-based clustering. Results given in this paper show that the proposed system can detect objects on the road very accurately and robustly.
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References
Benacer, I., Hamissi, A., Khouas, A.: A novel stereovision algorithm for obstacles detection based on U-V-disparity approach. In: 2015 IEEE International Symposium on Circuits and Systems (ISCAS), Lisbon, pp. 369–372 (2015). https://doi.org/10.1109/ISCAS.2015.7168647
Shah, M.: Fundamentals of Computer Vision (1997)
Lazaros, N., Sirakoulis, G.C., Gasteratos, A.: Review of stereo vision algorithms: from software to hardware. Int. J. Optomechatron. 2, 435–462 (2008). https://doi.org/10.1080/15599610802438680
Kumari, D., Kaur, K.: A survey on stereo matching techniques for 3D vision in image processing. Int. J. Eng. Manuf. (IJEM) 6(4), 40–49 (2016). https://doi.org/10.5815/ijem.2016.04.05
Kuhl, A.: Comparison of Stereo Matching Algorithms for Mobile Robots (2005)
Hata, K., Savarese, S.: Epipolar geometry. In: Computer Vision, From 3D Reconstruction to Recognition (2018)
Kudryavtsev, A.V., Dembélé, S., Piat, N.: Stereo-image rectification for dense 3D reconstruction in scanning electron microscope. In: 2017 International Conference on Manipulation, Automation and Robotics at Small Scales (MARSS), Montreal, pp. 1–6 (2017). https://doi.org/10.1109/MARSS.2017.8001905
Labayrade, R., Aubert, D., Tarel, J.P.: Real time obstacle detection in stereovision on non flat road geometry through v-disparity representation. In: IEEE Intelligent Vehicle Symposium, vol. 2, pp. 646–651 (2002)
Pao, D.C.W., Li, H.F., Jayakumar, R.: Shapes recognition using the straight line Hough transform: theory and generalization. IEEE Trans. Pattern Anal. Mach. Intell. 14(11), 1076–1089 (1992). https://doi.org/10.1109/34.166622
Scharwächter, T., Enzweiler, M., Roth, S., Franke, U.: Stixmantics: a medium-level model for real-time semantic scene understanding. In: European Conference on Computer Vision (ECCV) (2014)
Gao, Y., Ai, X., Wang, Y., Rarity, J., Dahnoun, N.: U-V-disparity based obstacle detection with 3D camera and steerable filter. In: 2011 IEEE Intelligent Vehicles Symposium (IV), Baden-Baden, pp. 957–962 (2011). https://doi.org/10.1109/IVS.2011.5940425
Zhu, K., Li, J., Zhang, H.: Stereo vision based road scene segment and vehicle detection. In: Proceedings of 2nd International Conference on Information Technology and Electronic Commerce (2014). https://doi.org/10.1109/ICITEC.2014.7105591
Ester, M., Kriegel, H.P., Sander, J., Xu, X.: A density-based algorithm for discovering clusters in large spatial databases with noise. In: Proceedings of KDD 1996 (1996)
Yang, J., Chen, H.: The 3D reconstruction of face model with active structured light and stereo vision fusion. In: 2017 3rd IEEE International Conference on Computer and Communications (ICCC), Chengdu, China, pp. 1902–1906 (2017). https://doi.org/10.1109/CompComm.2017.8322869
Liu, X., Li, D., Liu, X., Wang, Q.: A method of stereo images rectification and its application in stereo vision measurement. In: 2010 Second IITA International Conference on Geoscience and Remote Sensing, Qingdao, pp. 169–172 (2010). https://doi.org/10.1109/IITA-GRS.2010.5602989
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Shrivastava, S. (2020). Stereo Vision Based Object Detection Using V-Disparity and 3D Density-Based Clustering. In: Arai, K., Kapoor, S. (eds) Advances in Computer Vision. CVC 2019. Advances in Intelligent Systems and Computing, vol 944. Springer, Cham. https://doi.org/10.1007/978-3-030-17798-0_33
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DOI: https://doi.org/10.1007/978-3-030-17798-0_33
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