Accurate pedestrian detection has an important role in automotive applications because, by issuing warnings to the driver and acting actively on the car brakes, it can save human lives and decrease the probability of injuries. In order to achieve adequate accuracy, detectors require training sets containing a very large number of negative samples, which can be challenging for the training algorithms of models like support vector machines (SVM). A common approach to deal with such large datasets is Hard Negative Mining (HNM), which avoids working on the full set by growing an active pool of mined samples. A more recent method is the Block-Circulant Decomposition, which achieves the accuracy of HNM at a lower computational cost by reformulating the problem in the Fourier domain. The method however results in additional memory, required during training by the FFT transform, which could be reduced significantly by using only the positive examples. To address the problem, this paper proposes two main contributions: (1) it shows that the circulant decomposition method works with the same performances when only the positive samples are used in the training phase (2) it compares the performance of a detection pipeline based on HOG features trained with either both all negative and positive samples or with only positive samples on the INRIA pedestrian dataset.
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