Overview of Deep Learning-Based Approaches for Human Image Classification and Detection in Mass Crowds
Yateesh Gutti1, D. Vishnu Vardhan2, Bandla Ramesh3, J Stalin Babu4, B. Vijayendra Reddy5
1Yateesh Gutti, Assistant Professor, Department of Computer Science and Engineering, Koneru Lakshmaiah Education Foundation, Guntur(D), (Andhra Pradesh), India.
2Dr. D. Vishnu Vardhan, Associate Professor, Department of ECE, JNTUACEA, Anantapur (Andhra Pradesh), India.
3Bandla Ramesh, Assistant Professor, Department of CSE, St.Martin’s Engineering College, Secunderabad (Telangana), India.
4J Stalin Babu, Assistant Professor, Department of CSE, KLEF, Vijayawada (Andhra Pradesh), India.
5B. Vijayendra Reddy, Assistant Professor, Department of CSE, KLEF, Vijayawada (Andhra Pradesh), India.
Manuscript received on 03 March 2026 | Revised Manuscript received on 09 March 2026 | Manuscript Accepted on 15 March 2026 | Manuscript published on 30 March 2026 | PP: 9-11 | Volume-13 Issue-3, March 2026 | Retrieval Number: 100.1/ijies.D123415040326 | DOI: 10.35940/ijies.D1234.13030326
Open Access | Editorial and Publishing Policies | Cite | Zenodo | OJS | Indexing and Abstracting
© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open-access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Applications include crowd monitoring, public safety, and behavioural analysis, made possible by the widespread use of deep learning, which has transformed human image classification and detection in large-crowd scenarios. With an emphasis on convolutional neural networks (CNNs), object detection frameworks such as YOLO and Faster R-CNN, and sophisticated architectures that integrate attention mechanisms and spatiotemporal analysis, this paper offers a thorough overview of recent deep learning-based techniques for identifying and categorising people in dense crowds. We highlight cutting-edge methods and their performance metrics while discussing important issues such as occlusions, fluctuating crowd densities, and real-time processing requirements. Furthermore, we propose a novel Density-Aware Attention Network (DAAN) that improves detection accuracy in dense crowds. In addition, the study discusses ethical issues like bias and privacy and suggests future paths of inquiry.
Keywords: Deep Learning, Human Detection, Crowd Analysis, Object Detection, Convolutional Neural Networks, YOLO, Faster R-CNN, Attention Mechanisms, Crowd Human Dataset.
Scope of the Article: Computer Science and Engineering