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Driver Safety Belt Detection Using In-depth Image Analysis

This research identifies if a driver is wearing a safety belt through image samples and advanced feature extraction techniques such as SIFT and HOG, along with classification using Support Vector Machine. The study presents statistics on miss rates and tools used for analysis.

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Driver Safety Belt Detection Using In-depth Image Analysis

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  1. An ANN Approach to Identify if Driver is Wearing Safety Belts Hanwen Chen 12/9/2013

  2. Image Samples • Both Negative and Positive samples; • Same angle, same resolution, pre-processed. • Feature Extraction • SIFT: Scale-invariant Feature Transform; • HOG: Histogram of Oriented Gradients. • Classifier (SVM) • Support Vector Machine.

  3. Image Samples: • Positive Samples • Negative Samples

  4. Scale-invariant Feature Transform

  5. Histogram of Oriented Gradients

  6. Support Vector Machine • We are familiar with that!

  7. Collect Samples • Feature Extraction • VLFeat Open-source Library • http://www.vlfeat.org/ • Classify • Statistics Toolbox (Matlab R2013b) • Neuron Network Toolbox (Matlab R2013b)

  8. With SIFT feature: • Confusion matrix: • Miss rate: 30% • With HOG feature: • Confusion Matrix: • Miss rate: 30%

  9. Thank you!

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