Pedestrian Detection

Authors

  • Rajavel JM Department of Artificial Intelligence and Machine Learning, Sri Shakthi Institute of Engineering and Technology, Coimbatore, India Author
  • Shriram Keshav RM Department of Artificial Intelligence and Machine Learning, Sri Shakthi Institute of Engineering and Technology, Coimbatore, India Author
  • Shashaank G Department of Artificial Intelligence and Machine Learning, Sri Shakthi Institute of Engineering and Technology, Coimbatore, India Author
  • Mrs. S. Pushpakumari Department of Artificial Intelligence and Machine Learning, Sri Shakthi Institute of Engineering and Technology, Coimbatore, India Author

DOI:

https://doi.org/10.32628/CSEIT25113377

Keywords:

Deep Learning, Object Detection, YOLOv4-tiny, Pedestrian Detection, Real-time Surveillance, OpenCV, DNN Module

Abstract

In an era of rapid urbanization and automation, pedestrian safety has become a central concern for surveillance and smart city infrastructure. This paper presents a lightweight yet efficient system for pedestrian detection using YOLOv4-tiny, optimized for real-time video analysis. The system integrates OpenCV with the cv2.dnn module and Python-based inference logic to detect and annotate pedestrian locations in video streams. With the use of confidence filtering and non-maximum suppression, the solution demonstrates high accuracy and frame-wise efficiency even in constrained environments. The results suggest that YOLOv4-tiny provides an effective balance between speed and precision for edge deployment.

Downloads

Download data is not yet available.

References

Redmon, J. et al. (2016). You Only Look Once: Unified, Real-Time Object Detection. CVPR.

Bochkovskiy, A. et al. (2020). YOLOv4: Optimal Speed and Accuracy of Object Detection. arXiv:2004.10934.

Lin, T. Y. et al. (2014). Microsoft COCO: Common Objects in Context. ECCV.

Dollar, P. et al. (2009). Pedestrian Detection: Evaluation of the State of the Art. IEEE TPAMI.

Zhang, S. et al. (2018). CityPersons Dataset. CVPR

Downloads

Published

09-06-2025

Issue

Vol. 11 No. 3 (2025): May-June

Section

Research Articles

License

Copyright (c) 2025 International Journal of Scientific Research in Computer Science, Engineering and Information Technology

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.