Towards reliable real-time person detection

We propose a robust real-time person detection system, which aims to serve as solid foundation for developing solutions at an elevated level of reliability. Our belief is that clever handling of input data correlated with efficacious training algorithms are key for obtaining top performance. We introduce a comprehensive training method based on random sampling that compiles optimal classifiers with minimal bias and overfit rate. Building upon recent advances in multi-scale feature computations, our approach attains state-of-the-art accuracy while running at high frame rate.

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