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Pedestrian Detection

Author

Listed:
  • Rajavel JM
  • Shriram Keshav RM
  • Shashaank G
  • S. Pushpakumari

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.

Suggested Citation

  • Rajavel JM & Shriram Keshav RM & Shashaank G & S. Pushpakumari, 2025. "Pedestrian Detection," International Journal of Scientific Research in Computer Science, Engineering and Information Technology, International Journal of Scientific Research in Computer Science, Engineering and Information Technology, vol. 11(3), pages 941-944, June.
    • Handle: RePEc:jbh:ijsrcs:v11:y2025:i3:id:1551
    DOI: 10.32628/CSEIT25113377
    Note: Article URL: https://ijsrcseit.com/home/article/view/CSEIT25113377
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