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Moving object localization using optical flow for pedestrian detection from a moving vehicle.

Joko Hariyono1, Van-Dung Hoang1, Kang-Hyun Jo1

  • 1Graduate School of Electrical Engineering, University of Ulsan, Ulsan 680-749, Republic of Korea.

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This study introduces a novel pedestrian detection system for moving vehicles, utilizing optical flows and Histogram of Oriented Gradients (HOG) for enhanced accuracy. The method effectively identifies pedestrians by analyzing motion and shape features, outperforming existing techniques.

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Area of Science:

  • Computer Vision
  • Machine Learning
  • Robotics

Background:

  • Pedestrian detection is crucial for autonomous driving safety.
  • Existing methods struggle with dynamic environments and occlusions.

Purpose of the Study:

  • To develop an accurate pedestrian detection system for moving vehicles.
  • To improve detection performance by integrating optical flow and HOG features.

Main Methods:

  • Compensating for camera egomotion to isolate relative motion.
  • Extracting optical flows by tracking grid cells across consecutive frames.
  • Utilizing affine transformation for robust optical flow estimation.
  • Applying morphological processing to identify candidate human regions.
  • Employing Histogram of Oriented Gradients (HOG) features for classification.
  • Classifying candidate regions using a linear Support Vector Machine (SVM).

Main Results:

  • The proposed method successfully detects moving objects by segmenting regions with consistent optical flows.
  • Candidate human regions are identified and classified as pedestrians or non-pedestrians using HOG features and SVM.
  • Experimental results on the ETHZ pedestrian dataset show significant improvement over the original HOG method.

Conclusions:

  • The integrated approach of optical flow and HOG features provides a robust solution for pedestrian detection from a moving vehicle.
  • The method demonstrates enhanced accuracy and effectiveness in real-world scenarios.