Fast Human Detection Using a Cascade of Histograms of Oriented Gradients
Where Published: IEEE Computer Society Conference on Computer Vision and Pattern Recognition
We integrate the cascade-of-rejectors approach with Histograms of Oriented Gradients (HoG) features to achieve fast and accurate human detection system. Our features are HoGs of variable-size blocks that capture salient features of humans automatically. We find the appropriate set of blocks, from a large set of possible blocks, using AdaBoost as a feature selection. Finally, we use the integral image representation and a rejection cascade to greatly accelerate the performance of our system. Taken together our system can process frames at rates of between 5 to 30 frames per second, depending on the density in which we scan the image, while maintaining an accuracy level
similar to previously reported methods.
