Real time object tracking using deep learning and OpenCV

Research Article
Open access

Real time object tracking using deep learning and OpenCV

ZhiHao Jin 1* , Huanqing Yang 2
  • 1 Sichuan University    
  • 2 Xi'an Jiaotong University    
  • *corresponding author 2020141500198@stu.scu.edu.cn
Published on 4 February 2024 | https://doi.org/10.54254/2755-2721/35/20230406
ACE Vol.35
ISSN (Print): 2755-273X
ISSN (Online): 2755-2721
ISBN (Print): 978-1-83558-295-4
ISBN (Online): 978-1-83558-296-1

Abstract

This article summarizes the background and application fields of Open-Source Computer Vision Library (OpenCV) and deep learning and conducts research based on their object detection and tracking applications. The model algorithm selected in this article is the Convolutional Neural Networks (CNN) algorithm. CNN algorithm can be used for object detection in real-time scenarios. Moreover, the CNN algorithm has good credibility. The Python program developed based on CNN algorithm can effectively achieve real-time object tracking. The model shows good detection and tracking performance for trained targets and can be further applied in more specific scenarios in the future. Because deep learning can process large-scale data and recognize complex patterns, it can automatically learn and extract advanced features. Combining the two can achieve faster and more accurate detection and tracking of target objects. After having a large enough training sample size, it can detect and track the specified object more accurately. However, it is precisely due to the enormous sample size required for deep learning that there are still some difficulties in applying it to real-time object tracking. This article first discusses the use of supervised learning methods for deep understanding. When the input sample capacity is large enough, the machine can better reflect the real-time detection and tracking of the target object. But the time required will significantly increase. This issue still needs to be addressed in the subsequent research process.

Keywords:

deep learning, OpenCV, object detection, support vector machines, Convolutional Neural Networks

Jin,Z.;Yang,H. (2024). Real time object tracking using deep learning and OpenCV. Applied and Computational Engineering,35,272-279.
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References

[1]. A. Mangawati, Mohana, M. Leesan and H. V. R. Aradhya, "Object Tracking Algorithms for Video Surveillance Applications," 2018 International Conference on Communication and Signal Processing (ICCSP), Chennai, India, 2018, pp. 0667-0671, doi: 10.1109/ICCSP.2018.8524260.

[2]. A. Biswas, A. P. Jana, Mohana and S. Sai Tejas, "Classification of Objects in Video Records using Neural Network Framework," 2018 International Conference on Smart Systems and Inventive Technology (ICSSIT), Tirunelveli, India, 2018, pp. 564-569, doi: 10.1109/ICSSIT.2018.8748560.

[3]. F. K. Noble, "Comparison of OpenCV's feature detectors and feature matchers," 2016 23rd International Conference on Mechatronics and Machine Vision in Practice (M2VIP), Nanjing, China, 2016, pp. 1-6, doi: 10.1109/M2VIP.2016.7827292.

[4]. B. M U, H. Raghuram and Mohana, "Real Time Object Distance and Dimension Measurement using Deep Learning and OpenCV," 2023 Third International Conference on Artificial Intelligence and Smart Energy (ICAIS), Coimbatore, India, 2023, pp. 929-932, doi: 10.1109/ICAIS56108.2023.10073888.

[5]. V. Rajesh, U. P. Naik and Mohana, "Quantum Convolutional Neural Networks (QCNN) Using Deep Learning for Computer Vision Applications," 2021 International Conference on Recent Trends on Electronics, Information, Communication & Technology (RTEICT), Bangalore, India, 2021, pp. 728-734, doi: 10.1109/RTEICT52294.2021.9574030.

[6]. Pang, S., del Coz, J.J., Yu, Z. et al, “Deep Learning and Preference Learning for Object Tracking: A Combined Approach,” Neural Process Lett 47, pp.859-876, 2018, doi: 10.1007/s11063-017-9720-5.

[7]. X. Farhodov, O. -H. Kwon, K. W. Kang, S. -H. Lee and K. -R. Kwon, "Faster RCNN Detection Based OpenCV CSRT Tracker Using Drone Data," 2019 International Conference on Information Science and Communications Technologies (ICISCT), Tashkent, Uzbekistan, 2019, pp. 1-3, doi: 10.1109/ICISCT47635.2019.9012043.

[8]. V. Choudhary, P. Guha, K. Tripathi and S. Mishra, "Edge Detection of Variety of Cowpea Leaves Using OpenCV and Deep Learning," 2022 4th International Conference on Advances in Computing, Communication Control and Networking (ICAC3N), Greater Noida, India, 2022, pp. 1312-1316, doi: 10.1109/ICAC3N56670.2022.10074348.

[9]. W. Wang, J. Wang, Z. Zhang and D. Shi, "OpenCV Implementation of Image Processing Optimization Architecture of Deep Learning Algorithm based on Big Data Processing Technology," 2022 International Conference on Sustainable Computing and Data Communication Systems (ICSCDS), Erode, India, 2022, pp. 65-68, doi: 10.1109/ICSCDS53736.2022.9760795.

[10]. Tushar, K. Kumar and S. Kumar, "Object Detection using OpenCV and Deep Learning," 2022 4th International Conference on Advances in Computing, Communication Control and Networking (ICAC3N), Greater Noida, India, 2022, pp. 899-902, doi: 10.1109/ICAC3N56670.2022.10074012.

[11]. A. Sharma, J. Pathak, M. Prakash, and J. N. Singh, ‘Object Detection using OpenCV and Python’, in 2021 3rd International Conference on Advances in Computing, Communication Control and Networking (ICAC3N), Greater Noida, India: IEEE, Dec. 2021, pp. 501–505. doi: 10.1109/ICAC3N53548.2021.9725638.

[12]. K. O’Shea and R. Nash, ‘An Introduction to Convolutional Neural Networks’. arXiv, Dec. 02, 2015. Accessed: Jun. 25, 2023. [Online]. Available: http://arxiv.org/abs/1511.08458

[13]. S. Satpute, H. Shende, V. Shukla, and B. Patil, ‘Real Time Object Detection using Deep-Learning and OpenCV’, vol. 07, no. 04, 2020.

[14]. M. A. Hearst, S. T. Dumais, E. Osuna, J. Platt, and B. Scholkopf, ‘Support vector machines’, IEEE Intell. Syst. Their Appl., vol. 13, no. 4, pp. 18–28, Jul. 1998, doi: 10.1109/5254.708428.

[15]. G. Chandan, A. Jain, H. Jain, and Mohana, ‘Real Time Object Detection and Tracking Using Deep Learning and OpenCV’, in 2018 International Conference on Inventive Research in Computing Applications (ICIRCA), Coimbatore: IEEE, Jul. 2018, pp. 1305–1308. doi: 10.1109/ICIRCA.2018.8597266.

Cite this article

Jin,Z.;Yang,H. (2024). Real time object tracking using deep learning and OpenCV. Applied and Computational Engineering,35,272-279.

Data availability

The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.

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About volume

Volume title: Proceedings of the 2023 International Conference on Machine Learning and Automation

ISBN：978-1-83558-295-4(Print) / 978-1-83558-296-1(Online)
Editor：Mustafa İSTANBULLU
Conference website: https://2023.confmla.org/
Conference date: 18 October 2023
Series: Applied and Computational Engineering
Volume number: Vol.35
ISSN：2755-2721(Print) / 2755-273X(Online)

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References

[1]. A. Mangawati, Mohana, M. Leesan and H. V. R. Aradhya, "Object Tracking Algorithms for Video Surveillance Applications," 2018 International Conference on Communication and Signal Processing (ICCSP), Chennai, India, 2018, pp. 0667-0671, doi: 10.1109/ICCSP.2018.8524260.

[2]. A. Biswas, A. P. Jana, Mohana and S. Sai Tejas, "Classification of Objects in Video Records using Neural Network Framework," 2018 International Conference on Smart Systems and Inventive Technology (ICSSIT), Tirunelveli, India, 2018, pp. 564-569, doi: 10.1109/ICSSIT.2018.8748560.

[3]. F. K. Noble, "Comparison of OpenCV's feature detectors and feature matchers," 2016 23rd International Conference on Mechatronics and Machine Vision in Practice (M2VIP), Nanjing, China, 2016, pp. 1-6, doi: 10.1109/M2VIP.2016.7827292.

[4]. B. M U, H. Raghuram and Mohana, "Real Time Object Distance and Dimension Measurement using Deep Learning and OpenCV," 2023 Third International Conference on Artificial Intelligence and Smart Energy (ICAIS), Coimbatore, India, 2023, pp. 929-932, doi: 10.1109/ICAIS56108.2023.10073888.

[5]. V. Rajesh, U. P. Naik and Mohana, "Quantum Convolutional Neural Networks (QCNN) Using Deep Learning for Computer Vision Applications," 2021 International Conference on Recent Trends on Electronics, Information, Communication & Technology (RTEICT), Bangalore, India, 2021, pp. 728-734, doi: 10.1109/RTEICT52294.2021.9574030.

[6]. Pang, S., del Coz, J.J., Yu, Z. et al, “Deep Learning and Preference Learning for Object Tracking: A Combined Approach,” Neural Process Lett 47, pp.859-876, 2018, doi: 10.1007/s11063-017-9720-5.

[7]. X. Farhodov, O. -H. Kwon, K. W. Kang, S. -H. Lee and K. -R. Kwon, "Faster RCNN Detection Based OpenCV CSRT Tracker Using Drone Data," 2019 International Conference on Information Science and Communications Technologies (ICISCT), Tashkent, Uzbekistan, 2019, pp. 1-3, doi: 10.1109/ICISCT47635.2019.9012043.

[8]. V. Choudhary, P. Guha, K. Tripathi and S. Mishra, "Edge Detection of Variety of Cowpea Leaves Using OpenCV and Deep Learning," 2022 4th International Conference on Advances in Computing, Communication Control and Networking (ICAC3N), Greater Noida, India, 2022, pp. 1312-1316, doi: 10.1109/ICAC3N56670.2022.10074348.

[9]. W. Wang, J. Wang, Z. Zhang and D. Shi, "OpenCV Implementation of Image Processing Optimization Architecture of Deep Learning Algorithm based on Big Data Processing Technology," 2022 International Conference on Sustainable Computing and Data Communication Systems (ICSCDS), Erode, India, 2022, pp. 65-68, doi: 10.1109/ICSCDS53736.2022.9760795.

[10]. Tushar, K. Kumar and S. Kumar, "Object Detection using OpenCV and Deep Learning," 2022 4th International Conference on Advances in Computing, Communication Control and Networking (ICAC3N), Greater Noida, India, 2022, pp. 899-902, doi: 10.1109/ICAC3N56670.2022.10074012.

[11]. A. Sharma, J. Pathak, M. Prakash, and J. N. Singh, ‘Object Detection using OpenCV and Python’, in 2021 3rd International Conference on Advances in Computing, Communication Control and Networking (ICAC3N), Greater Noida, India: IEEE, Dec. 2021, pp. 501–505. doi: 10.1109/ICAC3N53548.2021.9725638.

[12]. K. O’Shea and R. Nash, ‘An Introduction to Convolutional Neural Networks’. arXiv, Dec. 02, 2015. Accessed: Jun. 25, 2023. [Online]. Available: http://arxiv.org/abs/1511.08458

[13]. S. Satpute, H. Shende, V. Shukla, and B. Patil, ‘Real Time Object Detection using Deep-Learning and OpenCV’, vol. 07, no. 04, 2020.

[14]. M. A. Hearst, S. T. Dumais, E. Osuna, J. Platt, and B. Scholkopf, ‘Support vector machines’, IEEE Intell. Syst. Their Appl., vol. 13, no. 4, pp. 18–28, Jul. 1998, doi: 10.1109/5254.708428.

[15]. G. Chandan, A. Jain, H. Jain, and Mohana, ‘Real Time Object Detection and Tracking Using Deep Learning and OpenCV’, in 2018 International Conference on Inventive Research in Computing Applications (ICIRCA), Coimbatore: IEEE, Jul. 2018, pp. 1305–1308. doi: 10.1109/ICIRCA.2018.8597266.

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