Zare, Hassan and Mousavi, Ebrahim (2025): A Novel Deep Learning Framework for Economic Video Analysis and Tactical Insight Extraction.
Preview |
PDF
MPRA_paper_127062.pdf Download (548kB) | Preview |
Abstract
This paper presents a novel deep learning framework for video analysis focused on automated key object detection and tactical action recognition within economic activity contexts. The proposed system integrates enhanced motion estimation for robust tracking of functional objects and state-of-the art 3D pose estimation to extract participant postures relevant to economic decision-making behavior. A deep semantic tactical ontology is employed to model the complex relationships between individuals, objects, and their actions, enabling interpretable and rule-based tactical insight extraction for economic interaction patterns beyond conventional classification. Evaluations conducted on benchmark datasets demonstrate high accuracy with approximately 91% in object detection and 96% in action recognition, highlighting the framework’s applicability to dynamic economic environments involving multi-agent interactions. Comparative analysis against baseline methods shows the effectiveness of the framework in handling complex scenarios with occlusions and rapidly changing economic behaviors. Future work will focus on enhancing preprocessing techniques, automating ontology rule learning, and extending the approach to a wider range of economically oriented domains. This research contributes to advancing intelligent analytics by bridging deep learning with semantic reasoning, fostering improved real-time tactical feedback and decision support in economic environments.
| Item Type: | MPRA Paper |
|---|---|
| Original Title: | A Novel Deep Learning Framework for Economic Video Analysis and Tactical Insight Extraction |
| English Title: | A Novel Deep Learning Framework for Economic Video Analysis and Tactical Insight Extraction |
| Language: | English |
| Keywords: | Economic Video Analysis; Tactical Action Recognition; Deep Learning; Semantic Ontology; 3D Pose Estimation |
| Subjects: | C - Mathematical and Quantitative Methods > C0 - General C - Mathematical and Quantitative Methods > C0 - General > C01 - Econometrics L - Industrial Organization > L0 - General L - Industrial Organization > L0 - General > L00 - General P - Economic Systems > P0 - General R - Urban, Rural, Regional, Real Estate, and Transportation Economics > R1 - General Regional Economics R - Urban, Rural, Regional, Real Estate, and Transportation Economics > R1 - General Regional Economics > R11 - Regional Economic Activity: Growth, Development, Environmental Issues, and Changes R - Urban, Rural, Regional, Real Estate, and Transportation Economics > R1 - General Regional Economics > R13 - General Equilibrium and Welfare Economic Analysis of Regional Economies |
| Item ID: | 127062 |
| Depositing User: | Prof. Hassan Zare |
| Date Deposited: | 23 Dec 2025 04:50 |
| Last Modified: | 23 Dec 2025 04:50 |
| References: | [1] Q. Yuan, X. Li, and S. Kleiven, “A novel framework for video-informed reconstructions of sports accidents: A case study correlating brain injury pattern from multimodal neuroimaging with finite element analysis,” Brain Multiphysics, Jun. 2024. [2] P. Narwal, N. Duhan, and K. K. Bhatia, “Dynamic and personalized video summarization towards sports entertainment,” Entertainment Computing, Sep. 2025. [3] G. Xv and X. Wu, “Temporal event localization in sports videos via self-supervised proposal generation and cross-modal fusion,” Intelligent Systems with Applications, Sep. 2025. [4] D. M. Davids, A. A. E. Raj, and C. S. Christopher, “SportSummarizer: A unified multimodal fusion transformer for context-aware sports video summarization,” Neurocomputing, Nov. 1, 2025. [5] C. Zhu-Tian et al., "Sporthesia: Augmenting Sports Videos Using Natural Language," IEEE Trans. Vis. Comput. Graphics, vol. 29, no. 1, pp. 918–928, Jan. 2023, doi: 10.1109/TVCG.2022.3209497. [6] Z. Xi, G. Shi, and L. Wu, “EIKA: Explicit \& implicit knowledge-augmented network for entity-aware sports video captioning,” Expert Systems with Applications, May 15, 2025. [7] S. Geetha, N. Ganesh, and U. Mishra, “High speed and tiny objects tracking system in racquet sports videos using deep learning with trajectory rectification feature,” Procedia Computer Science, 2025. [8] Y. Zhang, Y. Pi, and Y. Liu, “Application of video behavior fast detection based on wearable motion sensor devices in sports training,” Measurement: Sensors, Jun. 2024. [9] A. Banjar, H. Dawood, and B. Zeb, “Sports video summarization using acoustic symmetric ternary codes and SVM,” Applied Acoustics, Jan. 15, 2024. [10] M. Dunnhofer and C. Micheloni, “Visual tracking in camera-switching outdoor sport videos: Benchmark and baselines for skiing,” Comput. Vis. Image Understand., Jun. 2024. [11] R. Li and B. Bhanu, "Energy-Motion Features Aggregation Network for Players’ Fine-Grained Action Analysis in Soccer Videos," IEEE Trans. Circuits Syst. Video Technol., vol. 34, no. 2, pp. 955–972, Feb. 2024, doi: 10.1109/TCSVT.2023.3288565. [12] A. Feria-Madueño, G. Monterrubio-Fernández, and A. Carnero-Diaz, “The effect of a novel video game on young soccer players’ sports performance and attention: Randomized controlled trial,” JMIR Serious Games, 2024. [13] O. Trabelsi, M. A. Souissi, and A. Gharbi, “Enhancing classroom attention during Ramadan: The efficacy of instructional videos in sports science education,” Learning and Motivation, Feb. 2024. [14] J. Zhang, D. Han, and M. Zhang, “ChatMatch: Exploring the potential of hybrid vision–language deep learning approach for the intelligent analysis and inference of racket sports,” Computer Speech & Language, Jan. 2025. [15] Rakhshan, Mohsen, Navid Vafamand, Mokhtar Shasadeghi, Morteza Dabbaghjamanesh, and Amirhossein Moeini. "Design of networked polynomial control systems with random delays: sum of squares approach." International Journal of Automation and Control 10, no. 1 (2016): 73-86. [16] Z. Zhao, C. He, and X. Xu, “Self-adaptive uncertainty modeling and relation reasoning for cross-modal egocentric human action recognition in sports,” Computers and Electrical Engineering, Oct. 2025. [17] C. Muller and J. Bonnema, “Exploring the motivational drivers of extended reality (XR) sports and fitness gameplay intentions,” Computers in Human Behavior, Nov. 2025. [18] A. M. Mutawa, K. V. R. Kumar, and M. Murugappan, “Using artificial intelligence to predict the next deceptive movement based on video sequence analysis: A case study on a professional cricket player’s movements,” Journal of Engineering Research, online Jan. 26, 2025. [19] C.-Y. Chiu, C.-C. Chang, and C.-Y. Chiang, “Automatic video analysis of countermovement jump performance using a single uncalibrated camera,” Journal of Biomechanics, Jun. 2025. [20] Khazaei, Peyman, Morteza Dabbaghjamanesh, Ali Kalantarzadeh, and Hasan Mousavi. "Applying the modified TLBO algorithm to solve the unit commitment problem." In 2016 World Automation Congress (WAC), pp. 1-6. IEEE, 2016. [21] Esapour, Khodakhast, Farid Moazzen, Mazaher Karimi, Morteza Dabbaghjamanesh, and Abdollah Kavousi-Fard. "A novel energy management framework incorporating multi-carrier energy hub for smart city." IET Generation, Transmission \& Distribution 17, no. 3 (2023): 655-666. [22] C. Zheng and Y. Zhou, “Multi-modal IoT data fusion for real-time sports event analysis and decision support,” Alexandria Engineering Journal, Sep. 2025. [23] Tajalli, Seyede Zahra, Mohammad Mardaneh, Elaheh Taherian-Fard, Afshin Izadian, Abdollah Kavousi-Fard, Morteza Dabbaghjamanesh, and Taher Niknam. "DoS-resilient distributed optimal scheduling in a fog supporting IIoT-based smart microgrid." IEEE Transactions on Industry Applications 56, no. 3 (2020): 2968-2977. [24] H.-M. Qiu, H.-B. Zhang, and J.-X. Du, “Learning referee evaluation and assessing action quality from coarse to fine in diving sport,” Neurocomputing, Oct. 1, 2025. [25] Tahmasebi, Dorna, Morteza Sheikh, Morteza Dabbaghjamanesh, Tao Jin, Abdollah Kavousi-Fard, and Mazaher Karimi. "A security-preserving framework for sustainable distributed energy transition: Case of smart city." Renewable Energy Focus 51 (2024): 100631. [26] L. Cheng et al., "SNIL: Generating Sports News From Insights With Large Language Models," IEEE Trans. Vis. Comput. Graphics, vol. 31, no. 7, pp. 3973–3986, July 2025, doi: 10.1109/TVCG.2024.3392683. [27] Dabbaghjamanesh, Morteza, Amirhossein Moeini, Abdollah Kavousi-Fard, and Alireza Jolfaei. "Real-time monitoring and operation of microgrid using distributed cloud–fog architecture." Journal of Parallel and Distributed Computing 146 (2020): 15-24. [28] A. Kim and S.-S. Kim, “Engaging in sports via the metaverse? An examination through analysis of metaverse research trends in sports,” Data Science and Management, Sep. 2024. [29] Dabbaghjamanesh, Morteza, Amirhossein Moeini, Abdollah Kavousi-Fard, and Alireza Jolfaei. "Real-time monitoring and operation of microgrid using distributed cloud–fog architecture." Journal of Parallel and Distributed Computing 146 (2020): 15-24. [30] M. L. Reinhard, D. L. Mann, and O. Höner, “The role of generic cognitive skills: An empirical investigation into the association between generic and sport-specific cognitive skills and playing level in youth football,” Journal of Science and Medicine in Sport, Jul. 2025. [31] Kavousi-Fard, Abdollah, Saeed Nikkhah, Motahareh Pourbehzadi, Morteza Dabbaghjamanesh, and Amir Farughian. "IoT-based data-driven fault allocation in microgrids using advanced µPMUs." Ad Hoc Networks 119 (2021): 102520. [32] I. Pattnaik and P. Narwal, “Implicit embedding based multi modal attention network for cricket video summarization,” Engineering Applications of Artificial Intelligence, May 15, 2025. [33] Dabbaghjamanesh, Morteza, Abdollah Kavousi-Fard, and Jie Zhang. "Stochastic modeling and integration of plug-in hybrid electric vehicles in reconfigurable microgrids with deep learning-based forecasting." IEEE Transactions on Intelligent Transportation Systems 22, no. 7 (2020): 4394-4403. [34] F. Wu et al., "A Survey on Video Action Recognition in Sports: Datasets, Methods and Applications," IEEE Trans. Multimedia, vol. 25, pp. 7943–7966, 2023, doi: 10.1109/TMM.2022.3232034. [35] Dabbaghjamanesh, Morteza, Shahab Mehraeen, Abdollah Kavousi-Fard, and Farzad Ferdowsi. "A new efficient stochastic energy management technique for interconnected AC microgrids." In 2018 IEEE Power \& Energy Society General Meeting (PESGM), pp. 1-5. IEEE, 2018. [36] Dabbaghjamanesh, Morteza, A. Moeini, M. Ashkaboosi, P. Khazaei, and K. Mirzapalangi. "High performance control of grid connected cascaded H-Bridge active rectifier based on type II-fuzzy logic controller with low frequency modulation technique." International Journal of Electrical and Computer Engineering 6, no. 2 (2016): 484. [37] Dabbaghjamanesh, Morteza, Abdollah Kavousi-Fard, and Shahab Mehraeen. "Effective scheduling of reconfigurable microgrids with dynamic thermal line rating." IEEE Transactions on Industrial Electronics 66, no. 2 (2018): 1552-1564. [38] D. Wang, D. Li, and M. Luan, “The neural dynamics of integrating prior and kinematic information during action anticipation in sport,” NeuroImage, Jul. 15, 2025. [39] Dabbaghjamanesh, Morteza, Abdollah Kavousi-Fard, Shahab Mehraeen, Jie Zhang, and Zhao Yang Dong. "Sensitivity analysis of renewable energy integration on stochastic energy management of automated reconfigurable hybrid AC–DC microgrid considering DLR security constraint." IEEE Transactions on Industrial Informatics 16, no. 1 (2019): 120-131. [40] C. Behlau, H. Pauly, and B. Strauss, “Are you thinking what I am thinking? A feasibility study of a virtual reality measurement for shared mental models in team sports,” Team Performance Management: An International Journal, Mar. 26, 2025. [41] Dabbaghjamanesh, Morteza, Boyu Wang, Abdollah Kavousi-Fard, Shahab Mehraeen, Nikos D. Hatziargyriou, Dimitris N. Trakas, and Farzad Ferdowsi. "A novel two-stage multi-layer constrained spectral clustering strategy for intentional islanding of power grids." IEEE Transactions on Power Delivery 35, no. 2 (2019): 560-570. [42] Dabbaghjamanesh, Morteza, Abdollah Kavousi-Fard, and Zhao Yang Dong. "A novel distributed cloud-fog based framework for energy management of networked microgrids." IEEE Transactions on Power Systems 35, no. 4 (2020): 2847-2862. [43] S. Kanimozhi, A. Sasithradevi and L. Sairamesh, "Ontology-Based Action Recognition in Sport Videos Using Semantic Verification Model," IEEE Access, vol. 12, pp. 96783–96796, 2024, doi: 10.1109/ACCESS.2024.3427858. |
| URI: | https://mpra.ub.uni-muenchen.de/id/eprint/127062 |

