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A Mechanism on Video Tracking and Recognition with Fast Transmission in Wireless Communications

by Jianjun Yang, Mingyuan Yan, Abi Salimi, Jason Porter, Ying Luo, Ju Shen
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 185 - Number 14
Year of Publication: 2023
Authors: Jianjun Yang, Mingyuan Yan, Abi Salimi, Jason Porter, Ying Luo, Ju Shen
10.5120/ijca2023922816

Jianjun Yang, Mingyuan Yan, Abi Salimi, Jason Porter, Ying Luo, Ju Shen . A Mechanism on Video Tracking and Recognition with Fast Transmission in Wireless Communications. International Journal of Computer Applications. 185, 14 ( Jun 2023), 1-8. DOI=10.5120/ijca2023922816

@article{ 10.5120/ijca2023922816,
author = { Jianjun Yang, Mingyuan Yan, Abi Salimi, Jason Porter, Ying Luo, Ju Shen },
title = { A Mechanism on Video Tracking and Recognition with Fast Transmission in Wireless Communications },
journal = { International Journal of Computer Applications },
issue_date = { Jun 2023 },
volume = { 185 },
number = { 14 },
month = { Jun },
year = { 2023 },
issn = { 0975-8887 },
pages = { 1-8 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume185/number14/32761-2023922816/ },
doi = { 10.5120/ijca2023922816 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-07T01:26:01.581045+05:30
%A Jianjun Yang
%A Mingyuan Yan
%A Abi Salimi
%A Jason Porter
%A Ying Luo
%A Ju Shen
%T A Mechanism on Video Tracking and Recognition with Fast Transmission in Wireless Communications
%J International Journal of Computer Applications
%@ 0975-8887
%V 185
%N 14
%P 1-8
%D 2023
%I Foundation of Computer Science (FCS), NY, USA
Abstract

In recent years, video-based object tracking and recognition have become critical research areas in computer vision and network communication systems. This paper proposes a novel method for video-based object tracking and recognition over fast network transmission using newly developed improved CAMSHIFT and Meanshift algorithms. The new mechanism is not only applies to single object tracking and recognition, but also to multiple objects and objects with motion. The proposed system also utilizes a wireless network to transmit video streams from remote devices to a central server where the tracking and recognition processes take place. The developed approach has several advantages, including the ability to track objects in real-time over wireless networks with low latency and high reliability. The proposed system can be applied in various applications, such as security surveillance, traffic monitoring, and human-computer interaction. In addition, an efficient and effective implementation is designed utilizing a wireless network to transmit video streams where the tracking and recognition processes take place.

References
  1. Wang, M., Ni, B. and Yang, X., 2017. Recurrent modeling of interaction context for collective activity recognition. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 3048-3056)
  2. Ibrahim, M.S. and Mori, G., 2018. Hierarchical relational networks for group activity recognition and retrieval. In Proceedings of the European conference on computer vision (ECCV) (pp. 721-736).
  3. Wu, J., Wang, L., Wang, L., Guo, J. and Wu, G., 2019. Learning actor relation graphs for group activity recognition. In Proceedings of the IEEE/CVF Conference on computer vision and pattern recognition (pp. 9964-9974).
  4. Shu, X., Zhang, L., Sun, Y. and Tang, J., 2020. Host–parasite: Graph LSTM-in-LSTM for group activity recognition. IEEE transactions on neural networks and learning systems, 32(2), pp.663-674.
  5. Ehsanpour, M., Abedin, A., Saleh, F., Shi, J., Reid, I. and Rezatofighi, H., 2020. Joint learning of social groups, individuals action and sub-group activities in videos. In Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part IX 16 (pp. 177-195). Springer International Publishing.
  6. Vahora, S.A. and Chauhan, N.C., 2019. Deep neural network model for group activity recognition using contextual relationship. Engineering Science and Technology, an International Journal, 22(1), pp.47- 54.
  7. Srinivas, D. and Hanumaji, K., 2019. Analysis of various image feature extraction methods against noisy image: SIFT, SURF and HOG. J Eng Sci, 10(2), pp.32-36.
  8. Wu, L.F., Wang, Q., Jian, M., Qiao, Y. and Zhao, B.X., 2021. A comprehensive review of group activity recognition in videos. International Journal of Automation and Computing, 18, pp.334-350.
  9. Liu, Z., Abbas, A., Jing, B.Y. and Gao, X., 2012. WaVPeak: picking NMR peaks through waveletbased smoothing and volume-based filtering. Bioinformatics, 28(7), pp.914-920.
  10. Shen, J. and Cheung, S.C.S., 2013. Layer depth denoising and completion for structured-light rgb-d cameras. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 1187- 1194).
  11. Shen, J., Raghunathan, A., Sen-ching, S.C. and Patel, R., 2011, July. Automatic content generation for video self modeling. In 2011 IEEE International Conference on Multimedia and Expo (pp. 1-6). IEEE.
  12. Yang, J., Hua, K., Wang, Y., Wang, W., Wang, H. and Shen, J., 2014, April. Automatic objects removal for scene completion. In 2014 IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS) (pp. 553-558). IEEE.
  13. Cai, Q., Yin, Y. and Man, H., 2013, July. Dspm: Dynamic structure preserving map for action recognition. In 2013 IEEE international conference on multimedia and expo (ICME) (pp. 1-6). IEEE.
  14. Cai, Q., Yin, Y. and Man, H., 2013, September. Learning spatio-temporal dependencies for action recognition. In 2013 IEEE International Conference on Image Processing (pp. 3740-3744). IEEE.
  15. Zhang, J., Luo, X., Chen, C., Liu, Z. and Cao, S., 2014. A wildlife monitoring system based on wireless image sensor networks. Sensors and Transducers, 180(10), p.104.
  16. Gavrilyuk, K., Sanford, R., Javan, M. and Snoek, C.G., 2020. Actor-transformers for group activity recognition. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (pp. 839-848).
  17. Zalluhoglu, C. and Ikizler-Cinbis, N., 2020. Collective Sports: A multi-task dataset for collective activity recognition. Image and Vision Computing, 94, p.103870.
  18. Tang, Y., Wang, Z., Li, P., Lu, J., Yang, M. and Zhou, J., 2018, October. Mining semantics-preserving attention for group activity recognition. In Proceedings of the 26th ACM international conference on Multimedia (pp. 1283-1291).
  19. Han, Z., Zhang, R., Wen, L., Xie, X. and Li, Z., 2016, December. Moving object tracking method based on improved camshift algorithm. In 2016 International Conference on Industrial Informatics- Computing Technology, Intelligent Technology, Industrial Information Integration (ICIICII) (pp. 91-95). IEEE.
  20. Angadi, S. and Nandyal, S., 2020. A review on object detection and tracking in video surveillance. International Journal of Advanced Research in Engineering and Technology, 11(9).
  21. Liu, J., Sridharan, S. and Fookes, C., 2016. Recent advances in camera planning for large area surveillance: A comprehensive review. ACM Computing Surveys (CSUR), 49(1), pp.1-37.
  22. Harahap, M., Manurung, A., Prakoso, A. and Tambunan, M.F., 2019, July. Face tracking with camshift algorithm for detecting student movement in a class. In Journal of Physics: Conference Series (Vol. 1230, No. 1, p. 012018). IOP Publishing.
  23. Kanellakis, C. and Nikolakopoulos, G., 2017. Survey on computer vision for UAVs: Current developments and trends. Journal of Intelligent and Robotic Systems, 87, pp.141-168.
  24. Kim, J.S., Kim, M.G. and Pan, S.B., 2021. A study on implementation of real-time intelligent video surveillance system based on embedded module. EURASIP Journal on Image and Video Processing, 2021(1), pp.1-22.
  25. Lee, J. and Park, S.Y., 2021. PLF-VINS: Real-time monocular visual-inertial SLAM with point-line fusion and parallel-line fusion. IEEE Robotics and Automation Letters, 6(4), pp.7033-7040.
  26. Fran, A.R.J., 2004, July. CAMSHIFT Tracker Design Experiments with Intel OpenCV and SAI. In International Mass Spectrometry Conference.
  27. Du, S., Xu, H. and Li, T., 2020. Implementation of Camshift Target Tracking Algorithm Based on Hybrid Filtering and Multifeature Fusion. Journal of Sensors, 2020, pp.1-13.
  28. Almansour, N.A., Syed, H.F., Khayat, N.R., Altheeb, R.K., Juri, R.E., Alhiyafi, J., Alrashed, S. and Olatunji, S.O., 2019. Neural network and support vector machine for the prediction of chronic kidney disease: A comparative study. Computers in biology and medicine, 109, pp.101-111.
  29. Fei, Z., Yang, J. and Lu, H., 2015. Improving routing efficiency through intermediate target based geographic routing. Digital Communications and Networks, 1(3), pp.204-212.
Index Terms

Computer Science
Information Sciences

Keywords

Tracking Recognition Transmission Object tracking Probability density function Object detection Video Tracking Recognition Wireless Sensor Network Fast Transmission

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