Vehicle pose estimation for vehicle detection and tracking based on road direction

(1) * Adhi Prahara Mail (Universitas Ahmad Dahlan, Indonesia)
(2) Ahmad Azhari Mail (Universitas Ahmad Dahlan, Indonesia)
(3) Murinto Murinto Mail (Universitas Ahmad Dahlan, Indonesia)
*corresponding author

Abstract


Vehicle has several types and each of them has different color, size, and shape. The appearance of vehicle also changes if viewed from different viewpoint of traffic surveillance camera. This situation can create many possibilities of vehicle poses. However, the one in common, vehicle pose usually follows road direction. Therefore, this research proposes a method to estimate the pose of vehicle for vehicle detection and tracking based on road direction. Vehicle training data are generated from 3D vehicle models in four-pair orientation categories. Histogram of Oriented Gradients (HOG) and Linear-Support Vector Machine (Linear-SVM) are used to build vehicle detectors from the data. Road area is extracted from traffic surveillance image to localize the detection area. The pose of vehicle which estimated based on road direction will be used to select a suitable vehicle detector for vehicle detection process. To obtain the final vehicle object, vehicle line checking method is applied to the vehicle detection result. Finally, vehicle tracking is performed to give label on each vehicle. The test conducted on various viewpoints of traffic surveillance camera shows that the method effectively detects and tracks vehicle by estimating the pose of vehicle. Performance evaluation of the proposed method shows 0.9170 of accuracy and 0.9161 of balance accuracy (BAC).

Keywords


Vehicle pose; Road detection; Road direction; Vehicle detection; Vehicle tracking

   

DOI

https://doi.org/10.26555/ijain.v3i1.88
      

Article metrics

Abstract views : 415 | PDF views : 99

   

Cite

   

Full Text

Download

References


Y. Li, B. Li, B. Tian, and Q. Yao, “Vehicle Detection Based on the and– or Graph for Congested Traffic Conditions,” IEEE Trans. Intell. Transp. Syst., vol. 14, no. 2, pp. 984–993, Jun. 2013.

H. Van Pham and B.-R. Lee, “Front-view car detection and counting with occlusion in dense traffic flow,” Int. J. Control. Autom. Syst., vol. 13, no. 5, pp. 1150–1160, Oct. 2015.

A. Mukhtar, L. Xia, and T. B. Tang, “Vehicle Detection Techniques for Collision Avoidance Systems: A Review,” IEEE Trans. Intell. Transp. Syst., vol. 16, no. 5, pp. 2318–2338, Oct. 2015.

S. Sivaraman and M. M. Trivedi, “Vehicle Detection by Independent Parts for Urban Driver Assistance,” IEEE Trans. Intell. Transp. Syst., vol. 14, no. 4, pp. 1597–1608, Dec. 2013.

H. He, Z. Shao, and J. Tan, “Recognition of Car Makes and Models From a Single Traffic-Camera Image,” IEEE Trans. Intell. Transp. Syst., vol. 16, no. 6, pp. 3182–3192, Dec. 2015.

S. M. Elkerdawi, R. Sayed, and M. ElHelw, “Real-Time Vehicle Detection and Tracking Using Haar-Like Features and Compressive Tracking,” Springer International Publishing, 2014, pp. 381–390.

K.-H. Lee, J.-N. Hwang, and S.-I. Chen, “Model-Based Vehicle Localization Based on 3-D Constrained Multiple-Kernel Tracking,” IEEE Trans. Circuits Syst. Video Technol., vol. 25, no. 1, pp. 38–50, Jan. 2015.

Y. Tang, C. Zhang, R. Gu, P. Li, and B. Yang, “Vehicle detection and recognition for intelligent traffic surveillance system,” Multimed. Tools Appl., pp. 1–16, Mar. 2015.

T. Wu, B. Li, and S.-C. Zhu, “Learning And-Or Models to Represent Context and Occlusion for Car Detection and Viewpoint Estimation,” Jan. 2015.

C. Wang, Y. Fang, H. Zhao, C. Guo, S. Mita, and H. Zha, “Probabilistic Inference for Occluded and Multiview On-road Vehicle Detection,” IEEE Trans. Intell. Transp. Syst., vol. 17, no. 1, pp. 215–229, Jan. 2016.

Bin Tian, Ye Li, Bo Li, and Ding Wen, “Rear-View Vehicle Detection and Tracking by Combining Multiple Parts for Complex Urban Surveillance,” IEEE Trans. Intell. Transp. Syst., vol. 15, no. 2, pp. 597–606, Apr. 2014.

X. Cao, C. Wu, J. Lan, P. Yan, and X. Li, “Vehicle Detection and Motion Analysis in Low-Altitude Airborne Video Under Urban Environment,” IEEE Trans. Circuits Syst. Video Technol., vol. 21, no. 10, pp. 1522–1533, Oct. 2011.

A. Jazayeri, H. Cai, J. Y. Zheng, and M. Tuceryan, “Vehicle Detection and Tracking in Car Video Based on Motion Model,” IEEE Trans. Intell. Transp. Syst., vol. 12, no. 2, pp. 583–595, Jun. 2011.

K. Kovačić, E. Ivanjko, and H. Gold, “Real time vehicle detection and tracking on multiple lanes,” 2014.

J. M. Á. Alvarez and A. M. Lopez, “Road Detection Based on Illuminant Invariance,” IEEE Trans. Intell. Transp. Syst., vol. 12, no. 1, pp. 184–193, Mar. 2011.

J. Fritsch, T. Kuhnl, and F. Kummert, “Monocular Road Terrain Detection by Combining Visual and Spatial Information,” IEEE Trans. Intell. Transp. Syst., vol. 15, no. 4, pp. 1586–1596, Aug. 2014.

Y. He, H. Wang, and B. Zhang, “Color-Based Road Detection in Urban Traffic Scenes,” IEEE Trans. Intell. Transp. Syst., vol. 5, no. 4, pp. 309–318, Dec. 2004.

R. M. Haralick, K. Shanmugam, and I. Dinstein, “Textural Features for Image Classification,” IEEE Trans. Syst. Man. Cybern., vol. SMC-3, no. 6, pp. 610–621, Nov. 1973.

X. Du and K. K. Tan, “Vision-based approach towards lane line detection and vehicle localization,” Mach. Vis. Appl., vol. 27, no. 2, pp. 175–191, Feb. 2016.

L. C. León and R. Hirata, “Car detection in sequences of images of urban environments using mixture of deformable part models,” 2014.

N. Dalal and B. Triggs, “Histograms of Oriented Gradients for Human Detection,” in 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893.

C.-C. Chang and C.-J. Lin, “LIBSVM,” ACM Trans. Intell. Syst. Technol., vol. 2, no. 3, pp. 1–27, Apr. 2011.

A. Prahara and Murinto, “Car detection based on road direction on traffic surveillance image,” in 2016 2nd International Conference on Science in Information Technology (ICSITech), 2016, pp. 344–349.

T. Moranduzzo and F. Melgani, “Detecting Cars in UAV Images With a Catalog-Based Approach,” IEEE Trans. Geosci. Remote Sens., vol. 52, no. 10, pp. 6356–6367, Oct. 2014.

S. Madhogaria, P. Baggenstoss, M. Schikora, W. Koch, and D. Cremers, “Car detection by fusion of HOG and causal MRF,” IEEE Trans. Aerosp. Electron. Syst., vol. 51, no. 1, pp. 575–590, Jan. 2015.

B. D. Lucas and T. Kanade, “An iterative image registration technique with an application to stereo vision,” Proceedings of the 7th international joint conference on Artificial intelligence - Volume 2. Morgan Kaufmann Publishers Inc., pp. 674–679, 1981.

C. G. Weng and J. Poon, “A New Evaluation Measure for Imbalanced Datasets,” in Proceedings of the 7th Australasian Data Mining Conference - Volume 87, 2008, pp. 27–32.

G. Bradski, “The opencv library,” Dr. Dobb’s J. Softw. Tools, 2000.




Creative Commons License
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

___________________________________________________________
International Journal of Advances in Intelligent Informatics
ISSN 2442-6571  (print) | 2548-3161 (online)
Organized by Informatics Department - Universitas Ahmad Dahlan , and UTM Big Data Centre - Universiti Teknologi Malaysia
Published by Universitas Ahmad Dahlan
W : http://ijain.org
E : info@ijain.org, andri.pranolo@tif.uad.ac.id (paper handling issues)
     ijain@uad.ac.id, andri.pranolo.id@ieee.org (publication issues)

View IJAIN Stats

This work is licensed under a Creative Commons Attribution-ShareAlike 4.0