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We describe a general and exact method to considerably speed up linear object detection systems operating in a sliding, multi-scale window fashion, such as the individual part detectors of part-based models. The main bottleneck of many of those systems is the computational cost of the convolutions between the multiple rescalings of the image to process, and the linear filters. We make use of properties of the Fourier transform and of clever implementation strategies to obtain a speedup factor proportional to the filters’ sizes. The gain in performance is demonstrated on the well known Pascal VOC benchmark, where we accelerate the speed of said convolutions by an order of magnitude.
This paper presents an acceleration strategy for extracting convolutional features from images for object detection, with exact accuracy and large speed ups. This is done following the idea that the Fourier transform changes the convolution operator of an equation into a multiplication, and so, avoiding intense calculations. To make this strategy working, the authors also propose some analytically motivated implementations that take full advantage of memory and CPU resources. The result is that feature extraction can be reduced from 450ms using the baseline method to 55ms using their approach. A very interesting reading.
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