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In large-scale automatic traffic sign surveying systems, the primary computational effort is concentrated at the traffic sign detectionstage. This paper focuses on reducing the computational load of particularly the sliding window object detection algorithm which isemployed for traffic sign detection. Sliding-window object detectors often use a linear SVM to classify the features in a window. Inthis case, the classification can be seen as a convolution of the feature maps with the SVM kernel. It is well known that convolutioncan be efficiently implemented in the frequency domain, for kernels larger than a certain size. We show that by careful reorderingof sliding-window operations, most of the frequency-domain transformations can be eliminated, leading to a substantial increase inefficiency. Additionally, we suggest to use the overlap-add method to keep the memory use within reasonable bounds. This allowsus to keep all the transformed kernels in memory, thereby eliminating even more domain transformations, and allows all scales in amultiscale pyramid to be processed using the same set of transformed kernels. For a typical sliding-window implementation, we havefound that the detector execution performance improves with a factor of 5.3. As a bonus, many of the detector improvements fromliterature, e.g. chi-squared kernel approximations, sub-class splitting algorithms etc., can be more easily applied at a lower performancepenalty because of an improved scalability

A Frequency-Domain Implementation of a Sliding-Window Traffic Sign Detector for Large Scale Panoramic Datasets