Human Detection Based on SVM and Improved Histogram of Oriented Gradients

Abstract:

Considering the fact that original histogram of oriented gradients (HOG) cannot extract the body local features in large image regions, its features are improved when extracted, then more gradient information are extracted and feature description operators can be obtained which describe human detail features better in lager image regions or detection windows. Considering speed, we select support vector machine (SVM) using linear function kernel as a classifier. Combining with HOG extraction and SVM training, the process includes three steps: features extraction, training and detection. Experiments show that while maintaining a relatively satisfactory speed the human detection system improves detection accuracy.

• Periodical: Applied Mechanics and Materials (Volumes 380-384)
• Pages: 3862-3865
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.380-384.3862
• Online since: August 2013
• Authors: Li Hong Zhang
• Keywords: Human Detection, Improved Histogram of Oriented Gradients, Support Vector Machine (SVM)

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