An Efficient Detection of Suspicious Objects from Dynamic Video Surveillance by Fusion-based Multiview Deep Learning Techniques

Authors

  • Ramesh Chandra Poonia Department of Computer Science, Christ University, Delhi NCR, India
  • Kamal Upreti Department of Computer Science, Christ University, Delhi NCR, India
  • Nidhi Singh G. D. Goenka University, Gurgaon, Haryana, India
  • Jyoti Kesarwani United College of Engineering and Research, Prayagraj, Uttar Pradesh, India
  • Mohammad Shabbir Alam Department of Computer Science, College of Engineering and Computer Science, Jazan University, Jazan, KSA

DOI:

https://doi.org/10.13052/jmm1550-4646.2111

Keywords:

Deep learning, video surveillance, object detection, YOLO and fusion

Abstract

Real-time detections of suspicious objects are needed to identify for finding criminal activities and are used in immediate alert systems for public safety applications. Video surveillance systems use live, closed-circuit televisions (live CCTVs) for dynamic video capturing of objects. Finding criminal activities over the dynamic video data is an emerging surveillance problem. The deep learning techniques are tedious for detecting suspicious movable objects and criminal activities. YOLO (You Only Look Once) gives more prominent movable video object detection accuracy than conventional deep models, like Convolutional Neural Network (CNN), 3D CNN, and Convolutional LSTM. State-of-the-art YOLO models, YOLOv8n, YOLOv8s, and YOLOv8l, are emphasized for extracting and detecting object motion detection from the dynamic video. YOLO models use single-view deep learning to classify or detect objects. These models limit the accuracy of the detection of complex and dynamic objects of dynamic video data. This paper presents the Fusion-based Multiview deep learning techniques to overcome this issue. The experimental study demonstrates that the proposed methodology efficiently detects suspicious data objects more than the single-view deep models.

Downloads

Download data is not yet available.

Author Biographies

Ramesh Chandra Poonia, Department of Computer Science, Christ University, Delhi NCR, India

Ramesh Chandra Poonia is a Professor in the Department of Computer Science at CHRIST (Deemed to be University), NCR Delhi Campus, India. He is internationally recognized for his research in sustainable technologies, focusing on energy-efficient algorithms, cyber-physical systems, and computational intelligence, particularly machine learning and data analytics. His contributions rank him among the top 2% of scientists worldwide, as recognized by Stanford University and Elsevier. Dr. Poonia has completed two distinguished postdoctoral fellowships: one at the Cyber-Physical Systems Laboratory at the Norwegian University of Science and Technology (NTNU) in Norway, and another through an international collaboration aimed at predicting pandemic diseases using machine learning, involving Oakland University in the USA and Imam University in Saudi Arabia. He earned his Ph.D. in Computer Science from Banasthali University, India, in 2013, and an M.Tech. in Data Science and Engineering from the Birla Institute of Technology and Science (BITS), Pilani, India. With an extensive portfolio of research, Dr. Poonia has served as lead editor for numerous special issues, books, and proceedings with renowned publishers such as Springer, Taylor & Francis, and Elsevier. He is also an associate editor for the Journal of Sustainable Computing: Informatics and Systems (Elsevier) and serves as a series editor for Computational and Intelligent Systems (CRC Press). As the founder of the SUSCOM and ICSCPS conferences, he received the 2024 Research Innovation Award for his outstanding contributions to the advancement of sustainable and intelligent systems.

Kamal Upreti, Department of Computer Science, Christ University, Delhi NCR, India

Kamal Upreti is currently working as an Associate Professor in Department of Computer Science, CHRIST (Deemed to be University), Delhi NCR, Ghaziabad, India. He completed is B. Tech (Hons) Degree from UPTU, M. Tech (Gold Medalist), PGDM (Executive) from IMT Ghaziabad and PhD in Department of Computer Science & Engineering. He has completed Postdoc from National Taipei University of Business, TAIWAN funded by MHRD.

He has published 50+ Patents, 32+Magazine issues and 113+ Research papers in in various reputed Journals and international Conferences. His areas of Interest such as Modern Physics, Data Analytics, Cyber Security, Machine Learning, Health Care, Embedded System and Cloud Computing. He has published more than 45+ authored and edited books under CRC Press, IGI Global, Oxford Press and Arihant Publication. He is having enriched years’ experience in corporate and teaching experience in Engineering Colleges.

He worked with HCL, NECHCL, Hindustan Times, Dehradun Institute of Technology and Delhi Institute of Advanced Studies, with more than 15+ years of enrich experience in research, Academics and Corporate. He also worked in NECHCL in Japan having project – “Hydrastore” funded by joint collaboration between HCL and NECHCL Company. Dr. Upreti worked on Government project – “Integrated Power Development Scheme (IPDS)” was launched by Ministry of Power, Government of India with the objectives of Strengthening of sub-transmission and distribution network in the urban areas. He has completed work with Joint collaboration with GB PANT & AIIMS Delhi, under funded project of ICMR Scheme on Cardiovascular diseases prediction strokes using Machine Learning Techniques from year 2017–2020 of having fund of 80 Lakhs. He got 5 Lakhs fund from DST SERB for conducting International Conference, ICSCPS-2024, 13–14 Sept 2024. Recently, he got 10 Lakhs fund from AICTE – Inter-Institutional Biomedical Innovations and Entrepreneurship Program (AICTE-IBIP) for 2024–2026. He has attended as a Session Chair Person in National, International conference and key note speaker in various platforms such as Skill based training, Corporate Trainer, Guest faculty and faculty development Programme. He awarded as best teacher, best researcher, extra academic performer and Gold Medalist in M. Tech programme.

Nidhi Singh, G. D. Goenka University, Gurgaon, Haryana, India

Nidhi Singh is an accomplished academician with over 14 years of experience spanning teaching, training, and corporate sectors. Currently serving as an Assistant Professor at G.D. Goenka University in Haryana, India, she holds a Ph.D. in Management and an MBA in Information Technology and Marketing. Her expertise covers business analytics, information technology, and emerging fields such as machine learning, data visualization, and disruptive technologies in higher education. She has published over twenty research papers in reputed journals like SCOPUS, WOS, and ABDC. Dr. Singh also holds certifications from prestigious institutions, including Harvard Business School and the Indian Institute of Management Visakhapatnam, further solidifying her expertise in emerging technologies and business strategies.

Jyoti Kesarwani, United College of Engineering and Research, Prayagraj, Uttar Pradesh, India

Jyoti Kesarwani is an accomplished academic professional with a strong background in Computer Applications. I hold a Master of Computer Applications (MCA) and pursuing Ph.D. in the domain of computer science. Currently, I serve as an Assistant Professor at United College of Engineering and Research. I taught many subjects such as C Programming, Design and Analysis of Algorithms (DAA) and Artificial Intelligence (AI). I have developed a keen interest in the fields of Machine Learning (ML) and Deep Learning (DL). My dedication to education and research continues to inspire and shape the next generation of computer science profession.

Mohammad Shabbir Alam, Department of Computer Science, College of Engineering and Computer Science, Jazan University, Jazan, KSA

Mohammad Shabbir Alam is presently working as Senior Lecturer in College of Engineering and Computer Science, Jazan University (Public University), Jazan, Kingdom of Saudi Arabia. He received his Master in Computer Science & Applications (MCA) in years 2007 from Aligarh Muslim University, Aligarh, India. More than 15 years of academic and industry experiences in area of Computer Science and Information to Technology.

He has published 1 UK Patents, 2 German Patents and 4 Australian patents, 4 Books, 1 Book chapter and more than 30+ research papers in reputed international journals and national/international conference proceedings. He is an author of Data structure and Algorithm book. His areas of research interest include Deep learning, Blockchain, Machine Learning and Health Care.

References

S. L and C. S. Christopher, “Video Surveillance using Deep Learning – A Review,” 2019 International Conference on Recent Advances in Energy-efficient Computing and Communication (ICRAECC), Nagercoil, India, 2019, pp. 1-5, doi: 10.1109/ICRAECC43874.2019.8995084.

Upreti Kamal, Peng Sheng-Lung, Kshirsagar Pravin Ramdas, Chakrabarti Prasun, Al-Alshaikh Halah A., Sharma, A. K., Poonia Ramesh Chandra, (2023) A multi-model unified disease diagnosis framework for cyber healthcare using IoMT- cloud computing networks, Journal of Discrete Mathematical Sciences and Cryptography, 26:6, 1819–1834, DOI: 10.47974/JDMSC-1831.

Y. Lin, Z. Ning, J. Liu, M. Zhang, P. Chen and X. Yang, “Video steganography network based on 3DCNN,” 2021 International Conference on Digital Society and Intelligent Systems (DSInS), Chengdu, China, 2021, pp. 178–181, doi: 10.1109/DSInS54396.2021.9670614.

T. Akilan, Q. J. Wu, A. Safaei, J. Huo and Y. Yang, “A 3D CNN-LSTM-Based Image-to-Image Foreground Segmentation,” in IEEE Transactions on Intelligent Transportation Systems, vol. 21, no. 3, pp. 959–971, March 2020, doi: 10.1109/TITS.2019.2900426.

Aggarwal, D., Mittal, S., Upreti, K., and Nayak, P. (2024). Reward Based Garbage Monitoring and Collection System Using Sensors. Journal of Mobile Multimedia, 20(02), 391–410. https://doi.org/10.13052/jmm1550-4646.2026.

Diwan, T., Anirudh, G. and Tembhurne, J.V. Object detection using YOLO: challenges, architectural successors, datasets and applications. Multimed Tools Appl 82, 9243–9275 (2023). https://doi.org/10.1007/s11042-022-13644-y.

Upreti, K., Singh, P., Jain, D. et al. Progressive loss-aware fine-tuning stepwise learning with GAN augmentation for rice plant disease detection. Multimedia Tools Appl (2024). https://doi.org/10.1007/s11042-024-19255-z.

Hermens, F. Automatic object detection for behavioural research using YOLOv8. Behav Res (2024). https://doi.org/10.3758/s13428-024-02420-5.

Elhanashi, A., Dini, P., Saponara, S. et al. TeleStroke: real-time stroke detection with federated learning and YOLOv8 on edge devices. J Real-Time Image Proc 21, 121 (2024). https://doi.org/10.1007/s11554-024-01500-1.

P. Rathore, D. Kumar, S. Rajasegarar, M. Palaniswami and J. C. Bezdek, “A Scalable Framework for Trajectory Prediction,” in IEEE Transactions on Intelligent Transportation Systems, vol. 20, no. 10, pp. 3860–3874, Oct. 2019, doi: 10.1109/TITS.2019.2899179.

Gündüz, M.Ş., Işık, G. A new YOLO-based method for real-time crowd detection from video and performance analysis of YOLO models. J Real-Time Image Proc 20, 5 (2023). https://doi.org/10.1007/s11554-023-01276-w.

Yang, L., Chen, G. and Ci, W. Multiclass objects detection algorithm using DarkNet-53 and DenseNet for intelligent vehicles. EURASIP J. Adv. Signal Process. 2023, 85 (2023). https://doi.org/10.1186/s13634-023-01045-8.

Upreti, K., Kapoor, A., Hundekari, S., Upreti, S., Kaul, K., Kapoor, S., and Tiwari, A. (2024). Deep Dive Into Diabetic Retinopathy Identification: A Deep Learning Approach with Blood Vessel Segmentation and Lesion Detection. Journal of Mobile Multimedia, 20(02), 495–524. https://doi.org/10.13052/jmm1550-4646.20210.

X. Ni, Z. Ma, J. Liu, B. Shi and H. Liu, “Attention Network for Rail Surface Defect Detection via Consistency of Intersection-over-Union(IoU)-Guided Center-Point Estimation,” in IEEE Transactions on Industrial Informatics, vol. 18, no. 3, pp. 1694–1705, March 2022, doi: 10.1109/TII.2021.3085848.

Rajendra Prasad, K., Mohammed, M. and Noorullah, R.M. Visual topic models for healthcare data clustering. Evol. Intel. 14, 545–562 (2021). https://doi.org/10.1007/s12065-019-00300-y.

Umamakeswari, A., Angelus, J., Kannan, M., Rashikha, Bragadeesh, S.A. (2020). Action Recognition Using 3D CNN and LSTM for Video Analytics. In: Bhateja, V., Satapathy, S., Zhang, YD., Aradhya, V. (eds) Intelligent Computing and Communication. ICICC 2019. Advances in Intelligent Systems and Computing, vol 1034. Springer, Singapore. https://doi.org/10.1007/978-981-15-1084-7_51.

Duarte, F.F., Lau, N., Pereira, A., Reis, L.P. (2024). Study on LSTM and ConvLSTM Memory-Based Deep Reinforcement Learning. In: Rocha, A.P., Steels, L., van den Herik, J. (eds) Agents and Artificial Intelligence. ICAART 2023. Lecture Notes in Computer Science, vol. 14546. Springer, Cham. https://doi.org/10.1007/978-3-031-55326-4_11.

Bhatt, C., Kumar, I., Vijayakumar, V. et al. The state of the art of deep learning models in medical science and their challenges. Multimedia Systems 27, 599–613 (2021). https://doi.org/10.1007/s00530-020-00694-1

Lundervold, A.S., Lundervold, A.: An overview of deep learning in medical imaging focusing on MRI. Zeitschrift für Medizinische Physik. 29(2), 102–127 (2019)

Dev, Krishna, Zubair Ashraf, Pranab K. Muhuri, and Sandeep Kumar. Deep autoencoder based domain adaptation for transfer learning. Multimedia Tools and Applications 81, no. 16, 22379–22405 (2022).

Sirisha, U., Praveen, S.P., Srinivasu, P.N. et al. Statistical Analysis of Design Aspects of Various YOLO-Based Deep Learning Models for Object Detection. Int J Comput Intell Syst 16, 126 (2023). https://doi.org/10.1007/s44196-023-00302-w

Ningthoujam, R., Pritamdas, K., Singh, L.S. (2024). Comparative Study on YOLOv2 Object Detection Based on Various Pretrained Networks. In: Swain, B.P., Dixit, U.S. (eds) Recent Advances in Electrical and Electronic Engineering. ICSTE 2023. Lecture Notes in Electrical Engineering, vol 1071. Springer, Singapore. https://doi.org/10.1007/978-981-99-4713-3_18.

Zhao, B., Xie, N., Ge, J., Chen, W. (2023). Development of Object Identification APP Based on YoloV2. In: Atiquzzaman, M., Yen, N., Xu, Z. (eds) Proceedings of the 4th International Conference on Big Data Analytics for Cyber-Physical System in Smart City – Volume 1. BDCPS 2022. Lecture Notes on Data Engineering and Communications Technologies, vol. 167. Springer, Singapore. https://doi.org/10.1007/978-981-99-0880-6_5.

Tyagi, B., Nigam, S., Singh, R. (2023). Person Detection Using YOLOv3. In: Kumar, R., Verma, A.K., Sharma, T.K., Verma, O.P., Sharma, S. (eds) Soft Computing: Theories and Applications. Lecture Notes in Networks and Systems, vol 627. Springer, Singapore. https://doi.org/10.1007/978-981-19-9858-4_77.

Guo, B., Wang, H., Jin, L. et al. DCM3-YOLOv4: A Real-Time Multi-Object Detection Framework. Automot. Innov. 7, 283–299 (2024). https://doi.org/10.1007/s42154-023-00258-9.

Arkin, E., Yadikar, N., Xu, X. et al. A survey: object detection methods from CNN to transformer. Multimed Tools Appl 82, 21353–21383 (2023). https://doi.org/10.1007/s11042-022-13801-3.

Cao, W., Li, T., Liu, Q. et al. PANet: Pluralistic Attention Network for Few-Shot Image Classification. Neural Process Lett 56, 209 (2024). https://doi.org/10.1007/s11063-024-11638-5.

Nakhodnov, M.S., Kodryan, M.S., Lobacheva, E.M. et al. Loss Function Dynamics and Landscape for Deep Neural Networks Trained with Quadratic Loss. Dokl. Math. 106 (Suppl 1), S43–S62 (2022).

Parulekar, B., Singh, N. and Ramiya, A.M. Evaluation of segment anything model (SAM) for automated labelling in machine learning classification of UAV geospatial data. Earth Sci Inform (2024). https://doi.org/10.1007/s12145-024-01402-7.

Boehme, M.G., Al-Turjman, F. (2024). Enhancing Object Detection Capabilities: A Comprehensive Exploration and Finetuning of YOLOv5 Algorithm Across Diverse Datasets. In: Al-Turjman, F. (eds) The Smart IoT Blueprint: Engineering a Connected Future. AIoTSS 2024. Advances in Science, Technology & Innovation. Springer, Cham. https://doi.org/10.1007/978-3-031-63103-0_9.

Gupta, C., Gill, N.S., Gulia, P. et al. A novel finetuned YOLOv6 transfer learning model for real-time object detection. J Real-Time Image Proc 20, 42 (2023). https://doi.org/10.1007/s11554-023-01299-3.

Luo, X. et al. (2024). Improved YOLOv7-Tiny Insulator Defect Detection Based on Drone Images. In: Huang, DS., Zhang, X., Guo, J. (eds) Advanced Intelligent Computing Technology and Applications. ICIC 2024. Lecture Notes in Computer Science, vol. 14866. Springer, Singapore. https://doi.org/10.1007/978-981-97-5594-3_29.

Zhao, H., Zhou, Y., Zhang, L., Peng, Y., Hu, X., Peng, H. and Cai, X.. Mixed YOLOv3-LITE: A lightweight real-time object detection method. Sensors, 20(7), p. 1861 (2020).

Edmundo Casas, Leo Ramos, Cristian Romero, Francklin Rivas-Echeverría, A comparative study of YOLOv5 and YOLOv8 for corrosion segmentation tasks in metal surfaces, Array, Volume 22, 2024, 100351, ISSN 2590-0056.

https://universe.roboflow.com/cigarettesmokingdetection/faces-knifes-pistols.

Xiao, B., Nguyen, M. and Yan, W.Q. Fruit ripeness identification using YOLOv8 model. Multimed Tools Appl 83, 28039–28056 (2024). https://doi.org/10.1007/s11042-023-16570-9.

Simeth, A., Kumar, A.A. and Plapper, P. Flexible and robust detection for assembly automation with YOLOv5: a case study on HMLV manufacturing line. J Intell Manuf (2024). https://doi.org/10.1007/s10845-024-02411-5.

Downloads

Published

2025-04-07

How to Cite

Poonia, R. C. ., Upreti, K. ., Singh, N. ., Kesarwani, J. ., & Alam, M. S. . (2025). An Efficient Detection of Suspicious Objects from Dynamic Video Surveillance by Fusion-based Multiview Deep Learning Techniques. Journal of Mobile Multimedia, 21(01), 1–26. https://doi.org/10.13052/jmm1550-4646.2111

Issue

2025: Vol 21 Iss 1

Section

Articles

Most read articles by the same author(s)

1 2 > >>