Fast multiple human detection with neighborhood-based speciation differential evolution

Human detection plays a crucial role in a number of real world applications. Because of the popularity of smart car, Virtual Reality (VR) and other applications, strong demand of real-time detecting rises. The efficiency of a human detection algorithm becomes more crucial than ever before. In this work, a novel human detection framework combining the Histograms of Oriented Gradients (HOG) feature, Support Vector Machine and Neighborhood-based Speciation Differential Evolution (NSDE), is proposed in consideration of fast and accurate detection. Instead of inefficiently traversing and grouping all of the detecting windows as the conventional method, HOG-SVM-NSDE framework searches the whole image in a heuristic way with the unique niching strategy. Experiment results show that the HOG-SVM-NSDE framework achieves a favorable efficiency while still maintains a practical accuracy.

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