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.

📊 Article Downloads

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.

How to Cite

[1]
Rajavel JM, Shriram Keshav RM, Shashaank G, and Mrs. S. Pushpakumari, “Pedestrian Detection”, Int. J. Sci. Res. Comput. Sci. Eng. Inf. Technol, vol. 11, no. 3, pp. 941–944, Jun. 2025, doi: 10.32628/CSEIT25113377.