A comprehensive overview of deep learning models for object detection from videos/images

• URL: http://arxiv.org/abs/2601.14677v1
• Date: Wed, 21 Jan 2026 05:50:21 GMT
• Title: A comprehensive overview of deep learning models for object detection from videos/images
• Authors: Sukana Zulfqar, Sadia Saeed, M. Azam Zia, Anjum Ali, Faisal Mehmood, Abid Ali,
• Abstract summary: Object detection in video and image surveillance is a well-established yet rapidly influenced by recent deep learning advancements.<n>This review examines architectural innovations, generative model integration, and the use of temporal information to enhance robustness and accuracy.<n>The primary goal is to evaluate the current effectiveness of semantic object detection, while analysing deep learning models and their practical applications.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Object detection in video and image surveillance is a well-established yet rapidly evolving task, strongly influenced by recent deep learning advancements. This review summarises modern techniques by examining architectural innovations, generative model integration, and the use of temporal information to enhance robustness and accuracy. Unlike earlier surveys, it classifies methods based on core architectures, data processing strategies, and surveillance specific challenges such as dynamic environments, occlusions, lighting variations, and real-time requirements. The primary goal is to evaluate the current effectiveness of semantic object detection, while secondary aims include analysing deep learning models and their practical applications. The review covers CNN-based detectors, GAN-assisted approaches, and temporal fusion methods, highlighting how generative models support tasks such as reconstructing missing frames, reducing occlusions, and normalising illumination. It also outlines preprocessing pipelines, feature extraction progress, benchmarking datasets, and comparative evaluations. Finally, emerging trends in low-latency, efficient, and spatiotemporal learning approaches are identified for future research.

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