People Detection with Heterogeneous Features and Explicit Optimization on Computation Time

In this paper we present a novel people detector that employs discrete optimization for feature selection. Specifically, we use binary integer programming to mine heterogeneous features taking both detection performance and computation time explicitly into consideration. The final trained detector exhibits low Miss Rates with significant boost in frame rate. For example, it achieves a 2.6% less Miss Rate at 10-4 FPPW compared to Dalal and Triggs HOG detector with a 9.22x speed improvement.