Real-Time Objects Detection, Tracking, and Counting Using Image Processing Techniques
DOI:
https://doi.org/10.29194/NJES.26010024Keywords:
Real Time, Object Detection, Tracking and Counting, Traffic Surveillance, Image Processing TechniquesAbstract
As a result of the tremendous development taking place in modern systems and technologies in the field of electronic monitoring. Intelligent monitoring, decision making, and automated response systems have become common subjects at this time, especially after the development of machines responsible for these processes. Traffic surveillance is a trend goal nowadays using different techniques and equipment. In this article, real-time Object detection and tracking techniques were proposed for traffic surveillance using image processing techniques. A state was specifically examined for its ability to detect and count passing motorcycles on a highway in a specific area. The results showed good reliability, with a frame processing time of approximately about (30 ms) and the achievement of real-time performance. The main contribution of this article is reaching the best result implemented by the performance the real-time process using image process technique and tracking the object by depending on the sequencing of frames and can stands with rationally not so powerful machines. Several tools have been used for different types of necessary tasks that will be part of the required application such as Python 3.7; which was used to build the basic algorithms,Visual studio code (VSC) as an Integrated Development Environment (IDE), and Anaconda navigator for downloading many useful libraries. The specifications of the used device were Intel(R) Core (TM) i7- 10750H CPU @ 2.60GHz 2.59 GHz, RAM 16.0 GB, NVIDIA GeForce GTX 1650 GPU, 64-bit operating system, x64-based processor.
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References
Abdul Vahab, et al. "Applications of object detection system," International Research Journal of Engineering and Technology (IRJET), vol. 6, issue 4, pp. 4186-4192, April 2019.
https://www.irjet.net/archives/V6/i4/IRJET-V6I4920.pdf
Z. Zou, Z. Shi, Y. Guo and J. Ye. (2019, May). Object detection in 20 years: A survey. in arXiv:1905.05055v2 [online].
https://arxiv.org/abs/1905.05055
Benenson, R., Omran, M., Hosang, J., Schiele, B., "Ten years of pedestrian detection, what have we learned?," In: Computer Vision - ECCV 2014 Workshops, L. Agapito et al. (Eds.): ECCV 2014 Workshops, Part II, LNCS 8926, 2015, pp. 613–627, Springer International Publishing (2015).
https://doi.org/10.1007/978-3-319-16181-5_47
Dollár, P., Wojek, C., Schiele, B., Perona, P., "Pedestrian Detection: An Evaluation of the State of the Art," in IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 34, no. 4, pp. 743-761, April 2012.
https://doi.org/10.1109/TPAMI.2011.155
S. Yanushkevich, P. Wang, M. Gavrilova, M. Nixon and S. Srihari, Image Pattern Recognition: Synthesis and Analysis in Biometrics, Singapore: World Scientific, 2007.
https://www.nlb.gov.sg/biblio/12941648
Singla, N., "Motion detection based on frame difference method," International Journal of Information Computation Technology. Vol. 4, issue 15, pp. 1559–1565, 2014.
https://www.ripublication.com/irph/ijict_spl/ijictv4n15spl_10.pdf
Nayagam, M. Gomathy, and K. Ramar. "A survey on real time object detection and tracking algorithms," International Journal of Applied Engineering Research, vol.10, number 9, pp 8290-8297, 2015.
P. Tavallali, M. Yazdi and M. R. Khosravi, "An Efficient Training Procedure for Viola-Jones Face Detector," 2017 International Conference on Computational Science and Computational Intelligence (CSCI), 2017, pp. 828-831,
https://doi.org/10.1109/CSCI.2017.143
N. Dalal and B. Triggs, "Histograms of oriented gradients for human detection," 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05), San Diego, CA, USA, 2005, vol. 1, pp. 886-893.
https://doi.org/10.1109/CVPR.2005.177
D. G. Lowe, "Object recognition from local scale-invariant features," Proceedings of the Seventh IEEE International Conference on Computer Vision, Kerkyra, Greece, 1999, vol. 2, pp. 1150-1157.
https://doi.org/10.1109/ICCV.1999.790410
David G. Lowe, “Distinctive image features from scale-invariant keypoints,” International journal of computer vision, vol. 60, no. 2, pp. 91–110, 2004.
https://www.cs.ubc.ca/~lowe/papers/ijcv04.pdf
S. Belongie, J. Malik and J. Puzicha, "Shape matching and object recognition using shape contexts," in IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 24, no. 4, pp. 509-522, April 2002.
https://doi.org/10.1109/34.993558
P. Felzenszwalb, D. McAllester and D. Ramanan, "A discriminatively trained, multiscale, deformable part model," 2008 IEEE Conference on Computer Vision and Pattern Recognition, Anchorage, AK, USA, 2008, pp. 1-8.
https://doi.org/10.1109/CVPR.2008.4587597
P. F. Felzenszwalb, R. B. Girshick and D. McAllester, "Cascade object detection with deformable part models," 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, San Francisco, CA, USA, 2010, pp. 2241-2248.
https://doi.org/10.1109/CVPR.2010.5539906
Eikvil, Line. "Optical character recognition," citeseer. ist. psu. edu/142042. html 26 1993.
https://doi.org/10.3403/00116086U
Ravina Mithe, Supriya Indalkar, and Nilam Divekar. "Optical character recognition." International journal of recent technology and engineering (IJRTE), vol 2, issue 1, pp. 72-75, March 2013.
https://www.ijrte.org/wp-content/uploads/papers/v2i1/A0504032113.pdf
Simhambhatla, Ramesh; Okiah, Kevin; Kuchkula, Shravan; and Slater, Robert (2019) "Self-Driving Cars: Evaluation of Deep Learning Techniques for Object Detection in Different Driving Conditions," SMU Data Science Review: Vol. 2: No. 1, Article 23.
https://scholar.smu.edu/datasciencereview/vol2/iss1/23
Abhishek Gupta, Alagan Anpalagan, Ling Guan, Ahmed Shaharyar Khwaja, “Deep learning for object detection and scene perception in self-driving cars: Survey, challenges, and open issues,” Array, vol. 10, February 2021.
https://doi.org/10.1016/j.array.2021.100057.
S. H. Naghavi, C. Avaznia and H. Talebi, "Integrated real-time object detection for self-driving vehicles," 2017 10th Iranian Conference on Machine Vision and Image Processing (MVIP), 2017, pp. 154-158.
https://doi.org/10.1109/IranianMVIP.2017.8342340
H. Jiang and E. Learned-Miller, "Face Detection with the Faster R-CNN," 2017 12th IEEE International Conference on Automatic Face & Gesture Recognition (FG 2017), pp. 650-657.
https://doi.org/10.1109/FG.2017.82
B. Froba and A. Ernst, "Face detection with the modified census transform," Sixth IEEE International Conference on Automatic Face and Gesture Recognition, 2004. Proceedings, pp. 91-96.
https://doi.org/10.1109/AFGR.2004.1301514
C. P. Papageorgiou, M. Oren and T. Poggio, "A general framework for object detection," Sixth International Conference on Computer Vision (IEEE Cat. No.98CH36271), 1998, pp. 555-562.
https://doi.org/10.1109/ICCV.1998.710772
Tripathi, R.K., Jalal, A.S. & Agrawal, S.C. “Suspicious human activity recognition: a review,” Artif Intell Rev 50, pp. 283–339, 2018.
https://doi.org/10.1007/s10462-017-9545-7
S. W. Pienaar and R. Malekian, "Human Activity Recognition using Visual Object Detection," 2019 IEEE 2nd Wireless Africa Conference (WAC), 2019, pp. 1-5.
https://doi.org/10.1109/AFRICA.2019.8843417
Xu, Xin, Jinshan Tang, Xiaolong Zhang, Xiaoming Liu, Hong Zhang, and Yimin Qiu. "Exploring Techniques for Vision Based Human Activity Recognition: Methods, Systems, and Evaluation," Sensors 13, no. 2: pp. 1635-165, 2013.
https://doi.org/10.3390/s130201635
J.K. Aggarwal, Lu Xia, “Human activity recognition from 3D data: A review,” Pattern Recognition Letters, vol.48, pp.70-80, 2014.
https://doi.org/10.1016/j.patrec.2014.04.011.
C. Papageorgiou and T. Poggio, "Trainable pedestrian detection," Proceedings 1999 International Conference on Image Processing (Cat. 99CH36348), 1999, vol. 4, pp. 35-39.
https://doi.org/10.1109/ICIP.1999.819462
S. Walk, N. Majer, K. Schindler and B. Schiele, "New features and insights for pedestrian detection," 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2010, pp. 1030-1037.
https://doi.org/10.1109/CVPR.2010.5540102
Kamble, P.R., Keskar, A.G. & Bhurchandi, K.M. “Ball tracking in sports: a survey,” Artif Intell Rev 52, pp. 1655–1705, 2019.
https://doi.org/10.1007/s10462-017-9582-2
Arteta, C., Lempitsky, V., Noble, J.A., Zisserman, A. (2014). “Interactive Object Counting,” in Computer Vision – ECCV 2014. ECCV 2014. Lecture Notes in Computer Science, Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds). vol 8691. Springer, Cham.
https://doi.org/10.1007/978-3-319-10578-9_33.
C. Pornpanomchai, F. Stheitsthienchai and S. Rattanachuen, "Object Detection and Counting System," 2008 Congress on Image and Signal Processing, Sanya, China, 2008, pp. 61-65.
https://doi.org/10.1109/CISP.2008.108
A. Hanbury, “A survey of methods for image annotation,” Journal of Visual Languages & Computing, vol. 19, No. 5, pp.617-627, 2008.
https://doi.org/10.1016/j.jvlc.2008.01.002
Dengsheng Zhang, Md. Monirul Islam, Guojun Lu, “A review on automatic image annotation techniques,” Pattern Recognition, vol. 45, issue 1, pp. 346-362, January 2012.
https://doi.org/10.1016/j.patcog.2011.05.013
Jeon, J., Manmatha, R. (2004). “Using Maximum Entropy for Automatic Image Annotation,” in Image and Video Retrieval. CIVR 2004. Lecture Notes in Computer Science, Enser, P., Kompatsiaris, Y., O’Connor, N.E., Smeaton, A.F., Smeulders, A.W.M., EDS. vol 3115. Springer, Berlin, Heidelberg.
https://doi.org/10.1007/978-3-540-27814-6_7
Tom Fisk, (2020, August, 16th). Drone Footage Of Expressway During Daytime [online].
https://www.pexels.com/video/drone-footage-of-expressway-during-daytime-4673661
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