Abstract
Object detection algorithms have applications in various fields, including security, healthcare and defense. Because image-based object detection cannot exploit the rich temporal information inherent in video data, we suggest long-range video object pattern detection. Standard video-based object detectors use temporal context information to enhance object detection efficiency. However, object detection in challenging environments has received little attention. This paper proposes an improved You Only Look Once version 2 (YOLOv2) algorithms for object detection in surveillance videos, specifically vehicle detection and recognition. We reduced the number of parameters in the YOLOv2 base network and replaced it with LuNet. In the enhanced model, by using LuNet model for feature extraction to extract the most representative features from the image. LuNet is unique neural network architecture, a traditional and very promising algorithm for solving machine learning problems in video data frames. We perform numerous tests to evaluate the efficiency of the suggested approach, and our method outperforms conventional vehicle detection methods with an average accuracy of 96.41%. The study's findings demonstrate that the suggested technique achieves higher f-measure, precision, and error rate than other approaches.
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References
Akhtar M, Mahum R, Shafique BF, Amin R, El-Sherbeeny A, Lee S, Shaikh S (2022) A robust framework for object detection in a traffic surveillance system. Electronics 11:3425. https://doi.org/10.3390/electronics11213425
Ali R, Manikandan A, Xu J (2023) A Novel framework of adaptive fuzzy-GLCM segmentation and fuzzy with capsules network (F-CapsNet) classification. Neural Comput Appl. https://doi.org/10.1007/s00521-023-08666-y
Amit Kumar KC, Jacques L, de Vleeschouwer C (2017) Discriminative and efficient label propagation on complementary graphs for multi-object tracking. IEEE Trans Pattern Anal Mach Intell 39(1):61–74
Annamalai M, Bala MP (2023) Intracardiac mass detection and classification using double convolutional neural network classifier. J Eng Res 11(2A):272–280. https://doi.org/10.36909/jer.12237
Annamalai M, Muthiah PB (2022) An early prediction of tumor in heart by cardiac masses classification in echocardiogram images using robust back propagation neural network classifier. Braz Arch Biol Technol. https://doi.org/10.1590/1678-4324-2022210316
Balamurugan T, Gnanamanoharan E (2022) Brain tumor segmentation and classification using hybrid deep CNN with LuNet Classifier, PREPRINT (Version 1) available at Research Square. https://doi.org/10.21203/rs.3.rs-1599383/v1
Balamurugan D, Aravinth SS, Reddy PCS, Rupani A, Manikandan A (2022) Multiview objects recognition using deep learning-based wrap-CNN with voting scheme. Neural Process Lett 54:1–27. https://doi.org/10.1007/s11063-021-10679-4
Chen W, Sun Q, Wang J, Dong JJ, Xu C (2018) A novel model based on AdaBoost and deep CNN for vehicle classifcation. IEEE Access 6:60445–60455. https://doi.org/10.1109/ACCESS.2018.2875525
Dai J, Li Y, He K, Sun J (2016) R-FCN: object detection via region-based fully convolutional networks. In: Proceedings of the conference neural information processing systems (NIPS), Barcelona, Spain, 5–10 December
Dhiyanesh B, Rajkumar S, Radha R (2021) Improved object detection in video surveillance using deep convolutional neural network learning. In: 2021 Fifth international conference on i-SMAC (IOT in social, mobile, analytics and cloud) (I-SMAC), Palladam, India, pp. 1–8. https://doi.org/10.1109/I-SMAC52330.2021.9640894.
Giron NNF, Billones RKC, Fillone AM, Del Rosario JR, Bandala AA, Dadios EP (2020) Classification between pedestrians and motorcycles using fasterrcnn inception and SSD mobileNetv2. In: 2020 IEEE 12th international conference on humanoid, nanotechnology, information technology, communication and control, environment, pp 1–6. https://doi.org/10.1109/HNICEM51456.2020.9400113
Gurusamy K, Natarajan Y, Ijmtst E (2021) Improved object detection in video surveillance using deep convolutional neural network learning. Int J Modern Trends Sci Technol 7:104–108. https://doi.org/10.46501/IJMTST0711018
Han W, Khorrami P, Paine TL, Ramachandran P, Babaeizadeh M, Shi H, Li J, Yan S, Huang TS (2016) Seq-NMS for video object detection. In: Proceedings of the IEEE conference on computer vision and pattern recognition (CVPR), Las Vegas, NV, USA
Hu Q, Wang H, Li T, Shen C (2017) Deep CNNs with spatially weighted pooling for FNE grained car recognition. IEEE Trans Intell Transp Syst 18(11):3147–3156. https://doi.org/10.1109/TITS.2017.2679114
Ingle PY, 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
Jiang M, Pan Z, Tang Z (2017) Visual object tracking based on cross-modality Gaussian-Bernoulli deep Boltzmann machines with RGB-D sensors. Sensors 17(1):121
Khan S, AlSuwaidan L (2022) Agricultural monitoring system in video surveillance object detection using feature extraction and classification by deep learning techniques. Comput Electr Eng 102:2022
Khurana J, Aggarwal V, Singh H (2021) A comparative study of deep learning models for network intrusion detection. Int J Comput Appl 174:38–46. https://doi.org/10.5120/ijca2021921135
Kiran V, Dash S, Parida P (2021a) Improvement on deep features through various enhancement techniques for vehicles classification. Sens Imaging. https://doi.org/10.1007/s11220-021-00363-1
Kiran V, Dash S, Parida P (2021b) Vehicle recognition using extensions of pattern descriptors. IOP Conf Ser Mater Sci Eng 1166:012046. https://doi.org/10.1088/1757-899X/1166/1/012046
Kiran V, Dash S, Parida P (2022) Edge preserving noise robust deep learning networks for vehicle classification. Concurr Comput Pract Exp. https://doi.org/10.1002/cpe.7214
Kitti. Available online: http://www.cvlibs.net/datasets/kitti/eval_object.php. Accessed on 10 May 2012
Kolli S, Praveen Krishna AV, Ashok J, Manikandan A (2023) Internet of things for pervasive and personalized healthcare: architecture, technologies, components, applications, and prototype development. https://doi.org/10.4018/978-1-6684-8913-0.ch008
Lee H, Ullah I, Wan W, Gao Y, Fang Z (2019) Real-time vehicle make and model recognition with the residual squeezenet architecture. Sensors 19(15):982
Malik A, Rabbia M, Butt S, Rashid FA, Ahmed E-S, Seongkwan L, Sarang S (2022) A robust framework for object detection in a traffic surveillance system. Electronics 11:3425. https://doi.org/10.3390/electronics11213425
Mohana, Ravish Aradhya HV (2019) Object detection and tracking using deep learning and artificial intelligence for video surveillance applications. Int J Adv Comput Sci Appl (IJACSA). https://doi.org/10.14569/IJACSA.2019.0101269
Nagrath P, Thakur N, Jain R, Saini D, Hemanth J (2022). Understanding new age of intelligent video surveillance and deeper analysis on deep learning techniques for object tracking. https://doi.org/10.1007/978-3-030-89554-9_2
Palaniappan M, Annamalai M (2019) Advances in signal and image processing in biomedical applications. https://doi.org/10.5772/intechopen.88759
Połap D, Wózniak M (2021) Image features extractor based on hybridization of fuzzy controller and meta-heuristic. In: Proceedings of the 2021 IEEE international conference on fuzzy systems (FUZZ-IEEE), Luxembourg, pp 1–6
Prabu S, Gnanasekar JM (2022) Realtime object detection through M-ResNet in video surveillance system. Intell Autom Soft Comput 35(2):201
Prakasha PS, Rajakshmib RR, Kumaravel T (2021) Object detection in surveillance video. Turk J Comput Math Educ 12:9
Redmon J, Divvala S, Girshick R, Farhadi A (2016) You only look once: Unified, real-time object detection. In: Proceedings of the 2016 IEEE conference on computer vision and pattern recognition, Las Vegas, NV, USA, pp 779–788
Ren S, He K, Girshick R, Sun J (2017) Faster R-CNN: towards real-time object detection with region proposal networks. TPAMI 39:1137–1149
Shitrit HB, Berclaz J, Fleuret F, Fua P (2014) Multicommodity network flow for tracking multiple people. IEEE Trans Pattern Anal Mach Intell 36(8):1614–1627
Sowmya V, Radha R (2021) Heavy-vehicle detection based on YOLOv4 featuring data augmentation and transfer-learning techniques. J Phys Conf Ser 1911(1):012029
Sri Jamiya S (2021) An efficient algorithm for real-time vehicle detection using deep neural networks. Turk J Comput Math Educ TURCOMAT 12:2662–2676
Sudan J, Changho S, Eunmok Y, Gyanendra Prasad J (2021) Real time object detection and trackingsystem for video surveillance system. Multimedia Tools Appl 80:1–16. https://doi.org/10.1007/s11042-020-09749-x
Vehicle Data Set. Available online: https://www.kaggle.com/datasets/iamsandeepprasad/vehicle-data-set. Accessed on 12 April 2020
Venmathi AR, David S, Govinda E, Ganapriya K, Dhanapal R, Manikandan A (2023) An automatic brain tumors detection and classification using deep convolutional neural network with VGG-19. In: 2023 2nd International conference on advancements in electrical, electronics, communication, computing and automation (ICAECA). Coimbatore, India, pp 1–5. https://doi.org/10.1109/ICAECA56562.2023.10200949
Wang X, Zhang W, Wu X, Xiao L, Qian Y, Fang Z (2019) Real-time vehicle type classifcation with deep convolutional neural networks. J Real-Time Image Process 16(1):5–14. https://doi.org/10.1007/s11554-017-0712-5
Wang Z, Huang J, Xiong NN, Zhou X, Lin X, Ward TL (2020) A robust vehicle detection scheme for intelligent traffic surveillance systems in smart cities. IEEE Access 8:139299–139312
Wen L, Du D, Cai Z, Lei Z, Chang MC, Qi H, Lim J, Yang MH, Lyu S (2020) UA-DETRAC: A new benchmark and protocol for multi-object detection and tracking. Comput vis Image Understand 193:102907
Wu P, Guo H (2019) LuNet: a deep neural network for network intrusion detection. In: 2019 IEEE symposium series on computational intelligence (SSCI), pp 617–624. https://doi.org/10.1109/SSCI44817.2019.9003126.
Xiao Y, Jiang A, Ye J, Wang M-W (2020) Making of night vision: object detection under low-illumination. IEEE Access 8:123075–123086. https://doi.org/10.1109/ACCESS.2020.3007610
Zhang H, Tian Y, Wang K, Zhang W, Wang F-Y (2020) Mask SSD: An effective single-stage approach to object instance segmentation. IEEE Trans Image Process 29:2078–2093
Zhang P, Chen H, Li Q (2021) Research on vehicle recognition algorithm based on convolution neural network. J Phys Conf Ser 1865(4):042117
Zhao J, Hao S, Dai C, Zhang H, Zhao L (2022) Improved vision-based vehicle detection and classification by optimized YOLOv4. IEEE Access 10:8590–8603
Zhuo L, Jiang L, Zhu Z, Li J, Zhang J, Long H (2017) Vehicle classifcation for large scale trafc surveillance videos using convolutional neural networks. Mach Vis Appl 28(7):793–802. https://doi.org/10.1007/s00138-017-0846-2
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TM wrote the original draft and worked on the software. JR worked on the software. TM defined the methodology, reviewed, and edited the manuscript. TM supervised the work. All authors read and approved the final manuscript.
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Mohandoss, T., Rangaraj, J. Performance analysis of surveillance video object detection using LUNET algorithm. Int J Syst Assur Eng Manag (2024). https://doi.org/10.1007/s13198-024-02311-0
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DOI: https://doi.org/10.1007/s13198-024-02311-0