Evaluating deep learning–based computer vision techniques for robust object detection in video surveillance applications
*Dhirendra KumarCorresponding authorresearch.dhiren.in@gmail.comDepartment of Computer ScienceSchool of ScienceG H Raisoni UniversitySaikheda, Madhya Pradesh, 480337, IndiaView full profile → , Shailesh R. Thakareshaileshthakare2018@gmail.comDepartment of Computer ScienceSchool of ScienceG H Raisoni UniversitySaikheda, Madhya Pradesh, 480337, IndiaView full profile → , Aparna Kulkarniaapsk1988@gmail.comDepartment of Computer Science and ApplicationsSchool of Computer Science and EngineeringDr. Vishwanath Karad MIT World Peace UniversityPune, Maharashtra, 411038, IndiaView full profile →
* Corresponding author · click or hover a name for details
- Received:
- 01 Jan 2026
- Published Online:
- 31 Aug 2026
- Article type:
- Research Article
- Language:
- EN
- Article no.:
- JIOS-2318
- Pages:
- 2997–3006
Abstract
Video surveillance is widely used for automated security monitoring in public and private environments. Object detection enables intelligent analysis of surveillance footage by identifying entities such as pedestrians and vehicles. This paper presents a structured survey of object detection techniques used in video surveillance, covering both traditional motion-based methods and contemporary deep learning–based approaches. Conventional techniques, including background subtraction and optical flow, are discussed alongside deep learning frameworks such as Convolutional Neural Networks (CNNs), Region-based CNNs (R-CNN family), Single Shot Multibox Detector (SSD), and You Only Look Once (YOLO) [6, 7, 9, 12]. The study analyzes their detection accuracy, computational efficiency, and robustness under practical surveillance challenges such as occlusion, illumination variation, and real-time processing constraints. In addition, recent enhancements involving hybrid detection models, multi-object tracking, data augmentation, and lightweight architectures are examined [1, 5, 13, 14, 16]. The study also highlights open challenges and future research directions for improving robustness and real-time deployment.
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References
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