Enhanced human hitting movement recognition using motion history image and approximated ellipse techniques
Recognition of human hitting movement in a more specific context of sports like boxing is still a hard task because the existing systems use manual observation which could be easily flawed and highly inaccurate. However, in this study, an attempt is made to present an automated system designed for t...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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Association for Scientific Computing Electronics and Engineering (ASCEE)
2025
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| Online Access: | https://umpir.ump.edu.my/id/eprint/44340/ |
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| author | Mas Diyasa, I Gede Susrama Hanindia P, Made Mohd Amirul Syafiq, Zamri Agussalim, . Sayyidah, Humairah Septalian A, Denisa Umam, Faikul |
| author_facet | Mas Diyasa, I Gede Susrama Hanindia P, Made Mohd Amirul Syafiq, Zamri Agussalim, . Sayyidah, Humairah Septalian A, Denisa Umam, Faikul |
| author_sort | Mas Diyasa, I Gede Susrama |
| building | UMP Institutional Repository |
| collection | Online Access |
| description | Recognition of human hitting movement in a more specific context of sports like boxing is still a hard task because the existing systems use manual observation which could be easily flawed and highly inaccurate. However, in this study, an attempt is made to present an automated system designed for this purpose to detect a specific hitting movement commonly known as a punch using video input and image processing techniques. The system employs Motion History Image (MHI) to model trajectories of motions and combine them with other parameters to reconstruct movements which tend to have a temporal component. Thus, CCTV cameras set at different positions (front, back, left and right) enable the system to identify several types of punches including Jab, Hook, Uppercut and Combination punches. The most important aspect of this work is the proposal of MHI and the Ellipse approximation which is quicker in the integration of both than other sophisticated systems which take a considerable duration in computations. Therefore, the system classifies C_motion, Sigma Theta, and Sigma Rho parameters to distress hitting from non-hitting movements. Evaluation on a dataset captured from multiple viewpoints establishes that the system performs well achieving the goal of 93 percent when detecting both the hitting and the non-hitting motion. These results demonstrate the system’s superiority to the system based such detection methods. This study paves the way for other applications in realtime such as sports analysis, security surveillance, and healthcare requiring greater efficiency in and accuracy of human movement assessment. The focus of future work may be in the direction of improving the recognition of slower movements, also modifying the system for more dynamic conditions in the future. |
| first_indexed | 2025-11-15T03:58:56Z |
| format | Article |
| id | ump-44340 |
| institution | Universiti Malaysia Pahang |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-15T03:58:56Z |
| publishDate | 2025 |
| publisher | Association for Scientific Computing Electronics and Engineering (ASCEE) |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | ump-443402025-09-22T00:38:43Z https://umpir.ump.edu.my/id/eprint/44340/ Enhanced human hitting movement recognition using motion history image and approximated ellipse techniques Mas Diyasa, I Gede Susrama Hanindia P, Made Mohd Amirul Syafiq, Zamri Agussalim, . Sayyidah, Humairah Septalian A, Denisa Umam, Faikul TK Electrical engineering. Electronics Nuclear engineering Recognition of human hitting movement in a more specific context of sports like boxing is still a hard task because the existing systems use manual observation which could be easily flawed and highly inaccurate. However, in this study, an attempt is made to present an automated system designed for this purpose to detect a specific hitting movement commonly known as a punch using video input and image processing techniques. The system employs Motion History Image (MHI) to model trajectories of motions and combine them with other parameters to reconstruct movements which tend to have a temporal component. Thus, CCTV cameras set at different positions (front, back, left and right) enable the system to identify several types of punches including Jab, Hook, Uppercut and Combination punches. The most important aspect of this work is the proposal of MHI and the Ellipse approximation which is quicker in the integration of both than other sophisticated systems which take a considerable duration in computations. Therefore, the system classifies C_motion, Sigma Theta, and Sigma Rho parameters to distress hitting from non-hitting movements. Evaluation on a dataset captured from multiple viewpoints establishes that the system performs well achieving the goal of 93 percent when detecting both the hitting and the non-hitting motion. These results demonstrate the system’s superiority to the system based such detection methods. This study paves the way for other applications in realtime such as sports analysis, security surveillance, and healthcare requiring greater efficiency in and accuracy of human movement assessment. The focus of future work may be in the direction of improving the recognition of slower movements, also modifying the system for more dynamic conditions in the future. Association for Scientific Computing Electronics and Engineering (ASCEE) 2025 Article PeerReviewed pdf en cc_by_sa_4 https://umpir.ump.edu.my/id/eprint/44340/1/Enhanced%20human%20hitting%20movement%20recognition%20using%20motion%20history%20image.pdf Mas Diyasa, I Gede Susrama and Hanindia P, Made and Mohd Amirul Syafiq, Zamri and Agussalim, . and Sayyidah, Humairah and Septalian A, Denisa and Umam, Faikul (2025) Enhanced human hitting movement recognition using motion history image and approximated ellipse techniques. International Journal of Robotics and Control Systems, 5 (1). pp. 222-239. ISSN 2775-2658. (Published) https://doi.org/10.31763/ijrcs.v5i1.1599 https://doi.org/10.31763/ijrcs.v5i1.1599 https://doi.org/10.31763/ijrcs.v5i1.1599 |
| spellingShingle | TK Electrical engineering. Electronics Nuclear engineering Mas Diyasa, I Gede Susrama Hanindia P, Made Mohd Amirul Syafiq, Zamri Agussalim, . Sayyidah, Humairah Septalian A, Denisa Umam, Faikul Enhanced human hitting movement recognition using motion history image and approximated ellipse techniques |
| title | Enhanced human hitting movement recognition using motion history image and approximated ellipse techniques |
| title_full | Enhanced human hitting movement recognition using motion history image and approximated ellipse techniques |
| title_fullStr | Enhanced human hitting movement recognition using motion history image and approximated ellipse techniques |
| title_full_unstemmed | Enhanced human hitting movement recognition using motion history image and approximated ellipse techniques |
| title_short | Enhanced human hitting movement recognition using motion history image and approximated ellipse techniques |
| title_sort | enhanced human hitting movement recognition using motion history image and approximated ellipse techniques |
| topic | TK Electrical engineering. Electronics Nuclear engineering |
| url | https://umpir.ump.edu.my/id/eprint/44340/ https://umpir.ump.edu.my/id/eprint/44340/ https://umpir.ump.edu.my/id/eprint/44340/ |