A Novel Enhanced Algorithm for Efficient Human Tracking

Mehdi Gheisari, Zohreh Safari, Mohammad Almasi, Amir Hossein Pourishaban Najafabadi, Ragesh G K, Yang Liu, Aaqif Afzaal Abbasi, Seyed Mojtaba Hosseini Bamakan

Abstract


Tracking moving objects have been an issue in recent years of computer vision and Image processing. And human tracking makes it a more significant challenge. This category has various aspects and wide applications, such as autonomous deriving, human-robot interactions, and human movement analysis. One of the issues that have always made tracking algorithms difficult is their interaction with goal recognition methods, the mutable appearance of variable aims, and simultaneous tracking of multiple goals. In this paper, a method with high efficiency and higher accuracy compared to the previous methods for tracking just objects using imaging with the fixed camera is introduced. The proposed algorithm operates in four steps in such a way to identify a fixed background and removing noise from that. This background is used to subtract from movable objects. After that, while the image is being filtered, the shadows and noises of the filmed image are removed, and finally, using the bubble routing method, the mobile object will be separated and tracked. Experimental results indicate that the proposed model for detecting and tracking mobile objects works well and can improve the motion and trajectory estimation of objects in terms of speed and accuracy to a desirable level up to %10 in terms of accuracy compared with previous methods.


Keywords


Object Tracking; Movable Objects; Image Filter; Background Subtraction; Bubble Routing; Human tracking; Human detection; Deep learning.

References


AG. Bradski, “Real time face and object tracking as a component of a perceptual user interface,” Proceedings Fourth IEEE Workshop on Applications of Computer Vision. WACV98 (Cat. No.98EX201).

D. Comaniciu and P. Meer, “Mean shift: a robust approach toward feature space analysis,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 24, no. 5, pp. 603–619, 2002.

J. A. Corrales, P. Gil, F. A. Candelas, F. Torres. 2009. "Tracking based on Hue-Saturation Features with a Miniaturized Active Vision System". In Proceedings Book of 40th International Symposium on Robotics, Asociación Española de Robótica y Automatización Tecnologías de la Producción - AER-ATP, Barcelona, Spain. pp.107.

Tian, G., Hu, R., Wang, Z., and Fu, Y. 2009. "Improved Object Tracking Algorithm Based on New HSV Color Probability Model". In Proceedings of the 6th international Symposium on Neural Networks: Advances in Neural Networks - Part II, Wuhan, China.

Bradski, G., and Kaehler, A. 2008. Learning OpenCV: Computer Vision with the OpenCV Library. O'Reilly Media, Inc.

J. G. Allen, R. Y. D. Xu, and J. S. Jin. 2004. "Object tracking using camshift algorithm and multiple quantized feature spaces", in Proceedings of the PanSydney area workshop on Visual information processing, ser. ACM International Conference Proceeding Series, vol. 100. Darlinghurst, Australia: Australian Computer Society, Inc., pp. 3-7.

J. Ning, L. Zhang, David Zhang and C. Wu. 2009, "Robust Object Tracking using Joint Color-Texture Histogram". International Journal of Pattern Recognition and Artificial Intelligence, vol. 23, No. 7 (2009), World Scientific Publishing Company 1245-1263.

Qiu, X., Liu, S., Liu, F. 2009. Kernel-based Target Tracking with Multiple Features Fusion. Joint 48th IEEE Conference on Decision and Control and 28th Chinese Control Conference, Shanghai, P.R. China.

Ganoun, A., Ould-Dris, N., and Canals, R. 2006, "Tracking System Using CAMSHIFT and Feature Points". 14th European Signal Processing Conference (EUSIPCO 2006), Florence, Italy.

Stolkin, R., I. Florescu, M. Baron, C. Harrier and B. Kocherov. 2008. Efficient Visual Servoing with the ABCshift Tracking Algorithm. In: IEEE International Conference on Robotics and Automation, pp. 3219-3224, Pasadena, California, USA.

Xu, R Y D; Allen, J & Jin, J S. 2003. Robust real-time tracking of non-rigid objects, Conferences in Research and Practice in Information Technology, VIP'03, Sydney, Australia.

Almasi, M. (2016). "New method based on Image processing to enhanced the Accuracy and Precision Of table tennis player's performance". UCT Journal of Research in Science, Engineering and Technology, 4(3), 1-3.

M. Alimardani and M. Almasi, "Investigating the application of particle swarm optimization algorithm in the neural network to increase the accuracy of breast cancer prediction," International Journal of Computer Trends and Technology, vol. 68, no. 4, pp. 65–72, 2020. https://doi.org/10.14445/22312803/IJCTT-V68I4P112.

D. Chahyati, M. I. Fanany, and A. M. Arymurthy, “Tracking People by Detection Using CNN Features,” Procedia Computer Science, vol. 124, pp. 167–172, 2017.

M. Almasi, H. Fathi, S. Adel, and S. Samiee, “Human Action Recognition through the First-Person Point of view, Case Study Two Basic Task,” International Journal of Computer Applications, vol. 177, no. 24, pp. 19–23, 2019.

F. G. Yasar and H. Kusetogullari, “Underwater human body detection using computer vision algorithms,” 2018 26th Signal Processing and Communications Applications Conference (SIU), 2018.

K. L. Chan, “Detection of swimmer using dense optical flow motion map and intensity information,” Machine Vision and Applications, vol. 24, no. 1, pp. 75–101, 2012.

K. Fukunaga and L.D. Hostetler. 1975. "The estimation of the gradient of a density function, with applications in pattern recognition", IEEE Trans. Information Theory, vol. 21, pp. 32-40.

J. Fan, W. Xu, Y. Wu, and Y. Gong, “Human Tracking Using Convolutional Neural Networks,” IEEE Transactions on Neural Networks, vol. 21, no. 10, pp. 1610–1623, 2010.

M. Almasi, S. A. Ghaeinian, S. Samiee, and H. Fathi, “Investigating the Application of Human Motion Recognition for Athletics Talent Identification using the Head-Mounted Camera,” 2020 International Conference on Inventive Computation Technologies (ICICT), 2020.

P. Hidayatullah and H. Konik, “CAMSHIFT improvement on multi-hue object and multi-object tracking,” 3rd European Workshop on Visual Information Processing, 2011.

M. Shi, C. Yang, and D. Zhang, “A Novel Human-Machine Collaboration Model of an Ankle Joint Rehabilitation Robot Driven by EEG Signals,” Mathematical Problems in Engineering, vol. 2021, pp. 1–8, 2021.

M. Miao, X. Gao, and W. Zhu, “A Construction Method of Lower Limb Rehabilitation Robot with Remote Control System,” Applied Sciences, vol. 11, no. 2, p. 867, 2021.

“Illumination-Robust Foreground Extraction for Text Area Detection in Outdoor Environment,” KSII Transactions on Internet and Information Systems, vol. 11, no. 1, 2016.

P. Viola and M. Jones, “Rapid object detection using a boosted cascade of simple features,” Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. CVPR 2001.

S. Sharma, S. Verma, M. Kumar and L. Sharma, "Use of Motion Capture in 3D Animation: Motion Capture Systems, Challenges, and Recent Trends," 2019 International Conference on Machine Learning, Big Data, Cloud and Parallel Computing (COMITCon), 2019, pp. 289-294, doi: 10.1109/COMITCon.2019.8862448.

“Survey on Human Activity Prediction from Unfinished Video,” International Journal of Recent Trends in Engineering and Research, vol. 4, no. 3, pp. 290–296, 2018.

S. Stalder, H. Grabner, and L. Van Gool. 2009. Video from web. http://www.vision.ee.ethz.ch/boostingTrackers/contactBoosting.html (visited February 2010).

R Valenti, F Hageloh. Video from web. http://student.science.uva.nl/ ~rvalenti/uva/MIR/movies/soccer.avi (visited April 2020).

Gheisari, M., Esnaashari, M. (2017). A survey to face recognition algorithms: advantageous and disadvantageous. Journal Modern Technology & Engineering, V. 2(1), pp. 57-65.

M. Jafari, J. Wang, Y. Qin, M. Gheisari, A. S. Shahabi and X. Tao, "Automatic text summarization using fuzzy inference," 2016 22nd International Conference on Automation and Computing (ICAC), Colchester, 2016, pp. 256-260.

Rezaeiye, Payam Porkar, et al. "Statistical method used for doing better corneal junction operation." Advanced Materials Research. Vol. 548. Trans Tech Publications, 2012

Rezaeiye, Payam Porkar, et al. "Agent programming with object oriented (C++)." Electrical, Computer and Communication Technologies (ICECCT), 2017 Second International Conference on. IEEE, 2017.

M. M. Motahari Kia, J. A. Alzubi, M. Gheisari, X. Zhang, M. Rahimi and Y. Qin, "A Novel Method for Recognition of Persian Alphabet by Using Fuzzy Neural Network," in IEEE Access, vol. 6, pp. 77265-77271, 2018.

Alzubi J.A., Yaghoubi A., Gheisari M., Qin Y. (2018) Improve Heteroscedastic Discriminant Analysis by Using CBP Algorithm. In: Vaidya J., Li J. (eds) Algorithms and Architectures for Parallel Processing. ICA3PP 2018. Lecture Notes in Computer Science, vol 11335. Springer, Cham

Jayaraman Sethuraman, Jafar Alzubi, Ramachandran Manikandan, Mehdi Gheisari* and Ambeshwar Kumar, “Eccentric Methodology with Optimization to Unearth Hidden Facts of Search Engine Result Pages”, Recent Patents on Computer Science (2018) 11: 1. https://doi.org/10.2174/2213275911666181115093050

X. Zhang, F. Fan, M. Gheisari and G. Srivastava, "A Novel Auto-Focus Method for Image Processing Using Laser Triangulation," in IEEE Access, vol. 7, pp. 64837-64843, 2019. doi: 10.1109/ACCESS.2019.2914186

Noor, F, Sajid, A, Shah, SBH, Zaman, M, Gheisari, M, Mariappan, V. Bayesian estimation and prediction for Burr‐Rayleigh mixture model using censored data. Int J Commun Syst. 2019;e4094. https://doi.org/10.1002/dac.4094

M. Gheisari et al., "A Survey on Clustering Algorithms in Wireless Sensor Networks: Challenges, Research, and Trends," 2020 International Computer Symposium (ICS), Tainan, Taiwan, 2020, pp. 294-299, doi: 10.1109/ICS51289.2020.00065.

Movassagh, A.A., Alzubi, J.A., Gheisari, M. et al. Artificial neural networks training algorithm integrating invasive weed optimization with differential evolutionary model. J Ambient Intell Human Comput (2021).

Shao, Yongfu, et al. "Optimization of Ultrasound Information Imaging Algorithm in Cardiovascular Disease Based on Image Enhancement." Mathematical Problems in Engineering 2021 (2021).




DOI: http://doi.org/10.11591/ijict.v11i1.pp%25p

Refbacks

  • There are currently no refbacks.


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

View IJICT Stats