Multi-Object Detection for Real-time Video Surveillance Systems
Keywords:
Real-time video surveillance, Multi-object detection, Convolutional neural networks, Gaussian mixture model, Background subtraction, Mobile surveillance systems, Video sequence processing, Real-time detection, Low-light object detectionAbstract
Video surveillance systems play a crucial role in modern security, with real-time applications becoming increasingly important across various sectors. This paper proposes a novel method for detecting moving objects in real time using Convolutional Neural Networks (CNNs) combined with Gaussian Mixture Modeling (GMM) for background subtraction. The system processes video sequences, converting them into frames where moving objects are distinguished from the background. GMM is used to model the background, while CNNs extracts local optimal features to enhance object tracking accuracy. This method is particularly effective for applications such as operational robotics and military surveillance, which require real-time mobile detection systems. The proposed system was tested using custom video sequences, demonstrating significant improvements in object detection accuracy and reliability even in challenging conditions such as low-light environments. The results show improved performance in tracking moving objects, with the system successfully reducing noise and maintaining detection precision.
References
Thenmozhi, T., & Kalpana, A. M. (2020). Retracted: Adaptive motion estimation and sequential outline separation based moving object detection in video surveillance system, 76. https://doi.org/10.1016/j.micpro.2020.103084
Sengar, S. S., & Mukhopadhyay, S. (2020). Moving object detection using statistical background subtraction in wavelet compressed domain. Multimedia tools and applications, 79(9), 5919–5940. https://doi.org/10.1007/s11042-019-08506-z
Chauhan, A. K., & Krishan, P. (2013). Moving object tracking using gaussian mixture model and optical flow. International journal of advanced research in computer science and software engineering, 3(4), 243–246. https://api.semanticscholar.org/CorpusID:17721082
Mangal, S., & Kumar, A. (2017). Real time moving object detection for video surveillance based on improved GMM. International journal of advanced technology and engineering exploration, 4(26), 17. http://dx.doi.org/10.19101/IJATEE.2017.426004
Elhoseny, M. (2020). Multi-object detection and tracking (MODT) machine learning model for real-time video surveillance systems. Circuits, systems, and signal processing, 39(2), 611–630. https://doi.org/10.1007/s00034-019-01234-7
Kiaee, N., Hashemizadeh, E., & Zarrinpanjeh, N. (2019). Using GLCM features in Haar wavelet transformed space for moving object classification. IET intelligent transport systems, 13(7), 1148–1153. https://doi.org/10.1049/iet-its.2018.5192
Madhan, K., & Shanmugapriya, N. (2023). Real time object detection in video surveillance using fast-d algorithm. 2023 international conference on research methodologies in knowledge management, artificial intelligence and telecommunication engineering (RMKMATE) (pp. 1-6). IEEE. https://doi.org/10.1109/RMKMATE59243.2023.10369292
Ingle, P. Y., & Kim, Y. G. (2022). Real-time abnormal object detection for video surveillance in smart cities. Sensors, 22(10), 3862. https://doi.org/10.3390/s22103862
Iqbal, M. J., Iqbal, M. M., Ahmad, I., Alassafi, M. O., Alfakeeh, A. S., & Alhomoud, A. (2021). Real-time surveillance using deep learning. Security and communication networks, 2021(1), 6184756. https://doi.org/10.1155/2021/6184756
Wahab, F., Ullah, I., Shah, A., Khan, R. A., Choi, A., & Anwar, M. S. (2022). Design and implementation of real-time object detection system based on single-shoot detector and OpenCV. Frontiers in psychology, 13, 1039645. https://doi.org/10.3389/fpsyg.2022.1039645
Florinabel, D. J. (2020). Real-time image processing method to implement object detection and classification for remote sensing images. Earth science informatics, 13(4), 1065–1077. https://doi.org/10.1007/s12145-020-00486-1
Nguyen, M. T., Truong, L. H., Tran, T. T., & Chien, C. F. (2020). Artificial intelligence based data processing algorithm for video surveillance to empower industry 3.5. Computers & industrial engineering, 148, 106671. https://doi.org/10.1016/j.cie.2020.106671
Ammar, S., Bouwmans, T., Zaghden, N., & Neji, M. (2020). Deep detector classifier (DeepDC) for moving objects segmentation and classification in video surveillance. IET image processing, 14(8), 1490–1501. https://doi.org/10.1049/iet-ipr.2019.0769
Zeng, W., Xie, C., Yang, Z., & Lu, X. (2020). A universal sample-based background subtraction method for traffic surveillance videos. Multimedia tools and applications, 79, 22211-22234. https://doi.org/10.1007/s11042-020-08948-w
Qiu, M., & Li, X. (2019). A fully convolutional encoder-decoder spatial-temporal network for real-time background subtraction. IEEE access, 7, 85949–85958. https://doi.org/10.1109/ACCESS.2019.2925913
Strisciuglio, N., Azzopardi, G., & Petkov, N. (2019). Robust inhibition-augmented operator for delineation of curvilinear structures. IEEE transactions on image processing, 28(12), 5852–5866. https://doi.org/10.1109/TIP.2019.2922096
Farou, B., Kouahla, M. N., Seridi, H., & Akdag, H. (2017). Efficient local monitoring approach for the task of background subtraction. Engineering applications of artificial intelligence, 64, 1–12. https://doi.org/10.1016/j.engappai.2017.05.013
Sun, P., Lv, L., Qin, J., & Lin, L. (2019). Moving target detection based on multi-feature adaptive background model. 2019 IEEE international conference on mechatronics and automation (ICMA) (pp. 1610-1614). IEEE. https://doi.org/10.1109/ICMA.2019.8816282
Lee, H., Kim, H., & Kim, J. I. (2016). Background subtraction using background sets with image-and color-space reduction. IEEE transactions on multimedia, 18(10), 2093–2103. https://doi.org/10.1109/TMM.2016.2595262
Fratama, R. R., Partiningsih, N. D. A., Rachmawanto, E. H., Sari, C. A., & Andono, P. N. (2019). Real-time multiple vehicle counter using background subtraction for traffic monitoring system. 2019 international seminar on application for technology of information and communication (ISEMANTIC) (pp. 1–5). IEEE. https://doi.org/10.1109/ISEMANTIC.2019.8884277
Koodtalang, W., & Sangsuwan, T. (2019). The chicken’s legs size classification using image processing and deep neural network. 2019 first international symposium on instrumentation, control, artificial intelligence, and robotics (ICA-SYMP) (pp. 183–186). IEEE. https://doi.org/10.1109/ICA-SYMP.2019.8646193
Joy, F., & Vijayakumar, V. (2022). An improved Gaussian mixture model with post-processing for multiple object detection in surveillance video analytics. International journal of electrical and computer engineering systems, 13(8), 653–660. https://doi.org/10.32985/ijeces.13.8.5
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