Research on Automatic Detection Method of Basketball Flight Trajectory Based on Background Subtraction
Main Article Content
Abstract
Accurate trajectory detection of moving objects is of considerable importance for intelligent visual sensing and electromagnetic-assisted monitoring systems, where reliable feature extraction and signal interpretation directly affect perception performance. To achieve precise detection of basketball flight trajectories, this study proposes an automatic detection method based on background subtraction. The proposed framework first establishes the operational procedure of the background subtraction algorithm, employs a multi-feature fusion strategy to suppress motion shadows, and applies median filtering for image denoising. A dynamically updated background reference model is then constructed to maintain robustness under changing scene conditions. Subsequently, the gray-scale difference image is binarized using an entropy maximization criterion to determine the optimal segmentation threshold, thereby extracting the foreground target and accurately identifying the basketball flight trajectory. Experimental results demonstrate that the proposed method significantly improves both detection accuracy and real-time performance while reducing missed detections and false alarms compared with conventional approaches. The developed framework provides an effective solution for trajectory analysis in dynamic environments and offers useful technical insights for electromagnetic sensing, intelligent imaging, and signal processing applications requiring robust moving-object detection.
Downloads
Article Details
This work is licensed under a Creative Commons Attribution 4.0 International License.
Authors who publish with this journal agree to the following terms:
- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See The Effect of Open Access).
References
L. Borgia, “The Internet of Things vision: Key features, applications and open issues,” Computer Communications, vol. 54, pp. 1–31, 2014.
T. Ishitaki, R. Obukata, T. Oda, and L. Barolli, “Application of deep recurrent neural networks for prediction of user behavior in Tor networks,” in 2017 31st International Conference on Advanced Information Networking and Applications Workshops (WAINA), pp. 238–243, 2017, doi: 10.1109/WAINA.2017.63.
Y. Bian and X. Tang, “Abnormal detection in big data video with an improved autoencoder,” Computational Intelligence and Neuroscience, vol. 2021, e9861533, 2021, doi: 10.1155/2021/9861533.
S. Ding, S. Qu, and Y. Xi, “A long video caption generation algorithm for big video data retrieval,” Future Generation Computer Systems, vol. 93, pp. 583–595, 2019.
A. Djerida, Z. Zhao, and J. Zhao, “Background subtraction in dynamic scenes using the dynamic principal component analysis,” IET Image Processing, vol. 14, no. 2, pp. 245–255, 2020.
G. Cocorullo, P. Corsonello, and F. Frustaci, “Multimodal background subtraction for high-performance embedded systems,” Journal of Real-Time Image Processing, vol. 16, no. 5, pp. 1407–1423, 2019.
L. Xiying, L. Guoming, and J. Qianyin, “Dynamic background subtraction method based on spatio-temporal classification,” IET Computer Vision, vol. 12, no. 4, pp. 492–501, 2018.
E. Kepes, P. Porizka, and J. Klus, “Influence of baseline subtraction on laser-induced breakdown spectroscopic data,” Journal of Analytical Atomic Spectrometry, vol. 33, no. 12, pp. 2107–2115, 2018.
H. J. Giraldo-Zuluaga, A. Salazar, and A. Gomez, “Camera-trap images segmentation using multi-layer robust principal component analysis,” The Visual Computer, vol. 35, no. 3, pp. 335–347, 2019.
K. D. Prasad, C. K. Prasath, and D. Rajan, “Object detection in a maritime environment: Performance evaluation of background subtraction methods,” IEEE Transactions on Intelligent Transportation Systems, vol. 20, no. 5, pp. 1787–1802, 2019.
O. Elharrouss, A. Abbad, and D. Moujahid, “Moving target detection zone using a block-based background model,” IET Computer Vision, vol. 12, no. 1, pp. 86–94, 2018.
Jianyang Zheng and Yinhai, “Extracting roadway background image: Mode-based approach,” Transportation Research Record, vol. 1944, no. 1, pp. 82–88, 2018.
Siqi H.A.O. and Shaowu, “A multi-aircraft conflict detection and resolution method for 4-dimensional trajectory-based operation,” Chinese Journal of Aeronautics, vol. 148, no. 7, pp. 177–191, 2018.
M. Micheli, D. Farnocchia, and K. J. Meech, “Non-gravitational acceleration in the trajectory of 1I/2017 U1(‘Oumuamua),” Nature, vol. 559, no. 7713, pp. 223–226, 2018.
T. Praczyk, “Ship trajectory anomaly detection,” Intelligent Data Analysis, vol. 23, no. 5, pp. 1021–1040, 2019.
L. Liu, G. Han, and Y. He, “Fault-tolerant event region detection on trajectory pattern extraction for industrial wireless sensor networks,” IEEE Transactions on Industrial Informatics, vol. 16, no. 3, pp. 2072–2080, 2020.
J. Hu, S. Xiong, and J. Zha, “Lane detection and trajectory tracking control of autonomous vehicle based on model predictive control,” International Journal of Automotive Technology, vol. 21, no. 2, pp. 285–295, 2020.