Automatic Traffic Video Summarization for Indian Roads using Yolo
DOI:
https://doi.org/10.15662/IJEETR.2026.0802047Keywords:
YOLOv8, Traffic Video Summarization, Object Detection, Convolutional Neural Networks(CNN), Smart Traffic Management, Indian RoadsAbstract
Traffic surveillance systems generate a massive amount of video data on a daily basis, especially on Indian roads where traffic conditions are highly dynamic and complex due to heterogeneous vehicles, unpredictable pedestrian movement, and frequent rule violations. Manually monitoring and analyzing these long-duration traffic videos is time-consuming, inefficient, and prone to human error, making it difficult for authorities to extract meaningful insights in real time. Existing systems primarily focus on simple recording or basic motion detection and lack the ability to automatically identify and summarize important traffic events such as congestion, accidents, and violations. To address these limitations, the proposed system introduces an automated traffic video summarization framework using a hybrid YOLOv8 and Convolutional Neural Network (CNN) approach. YOLOv8 is employed for real-time object detection and localization of vehicles, pedestrians, and other road users, generating bounding boxes and confidence scores, while the CNN model extracts deep spatial and temporal features to analyze motion patterns and identify significant events across video frames. The system processes continuous traffic video input, extracts relevant frames, tracks detected objects, and selects key events based on predefined criteria to generate concise and informative video summaries. This approach reduces data redundancy, minimizes storage requirements, and enhances the efficiency of traffic monitoring systems. Experimental results demonstrate improved accuracy, faster processing, and reliable detection of critical traffic events, making the system suitable for real-world deployment in smart traffic management and surveillance applications. Overall, the proposed framework provides an efficient, scalable, and automated solution for transforming lengthy traffic videos into meaningful summaries, supporting better decision-making and improved road safety
References
1) A. Saraff et al., “Indian Traffic Surveillance Video Summarization Using YOLO and Multi-Level Masking,” IEEE Access, vol. 13, pp. 171371–171385, 2025.
2) M. Tahir, Y. Qiao, N. Kanwal, B. Lee, and M. N. Asghar, “Real-Time Event-Driven Road Traffic Monitoring System Using CCTV Video Analytics,” IEEE Access, vol. 11, pp. 139097–139111, 2023.
3) I. Shoer, B. Köprü, and E. Erzin, “Role of Audio in Video Summarization,” IEEE International Conference on Acoustics, Speech, and Signal Processing Workshops (ICASSPW), 2023.
4) B. S. Tung, P. T. Ngoc, D. D. Thanh, and N. H. Thinh, “AI-Based Video Analysis for Traffic Monitoring,” Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC), 2022.
5) A. Pramanik, S. K. Pal, J. Maiti, and P. Mitra, “Traffic Anomaly Detection and Video Summarization Using Spatio-Temporal Rough Fuzzy Granulation With Z-Numbers,” IEEE Transactions on Intelligent Transportation Systems, vol. 23, no. 12, pp. 24116–24125, 2022.
6) S. Mishra and D. Yadav, “Vehicle Detection in High-Density Traffic Using YOLOv5,” International Journal of Computer Vision Applications, 2024.
7) V. Jabade et al., “Smart CCTV Video Summarization Using YOLO and DeepSORT,” International Journal of Intelligent Systems, 2024.
8) C.Nagarajan and M.Madheswaran - ‘Stability Analysis of Series Parallel Resonant Converter with Fuzzy Logic Controller Using State Space Techniques’- Taylor &Francis, Electric Power Components and Systems, Vol.39 (8), pp.780-793, May 2011. DOI: 10.1080/15325008.2010.541746
9) C.Nagarajan and M.Madheswaran - ‘Experimental verification and stability state space analysis of CLL-T Series Parallel Resonant Converter’ - Journal of Electrical Engineering, Vol.63 (6), pp.365-372, Dec.2012. DOI: 10.2478/v10187-012-0054-2
10) C.Nagarajan and M.Madheswaran - ‘Performance Analysis of LCL-T Resonant Converter with Fuzzy/PID Using State Space Analysis’- Springer, Electrical Engineering, Vol.93 (3), pp.167-178, September 2011. DOI 10.1007/s00202-011-0203-9
11) S.Tamilselvi, R.Prakash, C.Nagarajan,“Solar System Integrated Smart Grid Utilizing Hybrid Coot-Genetic Algorithm Optimized ANN Controller” Iranian Journal Of Science And Technology-Transactions Of Electrical Engineering, DOI10.1007/s40998-025-00917-z,2025
12) S.Tamilselvi, R.Prakash, C.Nagarajan,“ Adaptive sliding mode control of multilevel grid-connected inverters using reinforcement learning for enhanced LVRT performance” Electric Power Systems Research 253 (2026) 112428, doi.org/10.1016/j.epsr.2025.112428
13) S.Thirunavukkarasu, C. Nagarajan, 2024, “Performance Investigation on OCF and SCF study in BLDC machine using FTANN Controller," Journal of Electrical Engineering And Technology, Volume 20, pages 2675–2688, (2025), doi.org/10.1007/s42835-024-02126-w
14) C. Nagarajan, M.Madheswaran and D.Ramasubramanian- ‘Development of DSP based Robust Control Method for General Resonant Converter Topologies using Transfer Function Model’- Acta Electrotechnica et Informatica Journal , Vol.13 (2), pp.18-31,April-June.2013, DOI: 10.2478/aeei-2013-0025.
15) C.Nagarajan and M.Madheswaran - ‘DSP Based Fuzzy Controller for Series Parallel Resonant converter’- Springer, Frontiers of Electrical and Electronic Engineering, Vol. 7(4), pp. 438-446, Dec.12. DOI 10.1007/s11460-012-0212-0.
16) C.Nagarajan and M.Madheswaran - ‘Experimental Study and steady state stability analysis of CLL-T Series Parallel Resonant Converter with Fuzzy controller using State Space Analysis’- Iranian Journal of Electrical & Electronic Engineering, Vol.8 (3), pp.259-267, September 2012.
17) C.Nagarajan and M.Madheswaran, “Analysis and Simulation of LCL Series Resonant Full Bridge Converter Using PWM Technique with Load Independent Operation” has been presented in ICTES’08, a IEEE / IET International Conference organized by M.G.R.University, Chennai.Vol.no.1, pp.190-195, Dec.2007
18) Suganthi Mullainathan, Ramesh Natarajan, “An SPSS and CNN modelling based quality assessment using ceramic materials and membrane filtration techniques”, Revista Materia (Rio J.) Vol. 30, 2025, DOI: https://doi.org/10.1590/1517-7076-RMAT-2024-0721
19) M Suganthi, N Ramesh, “Treatment of water using natural zeolite as membrane filter”, Journal of Environmental Protection and Ecology, Volume 23, Issue 2, pp: 520-530,2022
20) G. Sara and Milidh J., “Query-Driven Video Summarization Using Faster-RCNN and DPP,” Journal of Advanced AI Systems, 2025.
21) A. Padia et al., “Object Detection and Classification Framework for Indian Road Traffic,” Journal of Computer Vision Research, 2024.
22) Redmon, J., et al., “You Only Look Once: Unified, Real-Time Object Detection,” IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016.
