Robust Real-Time Face Detection
International Journal of Computer Vision
Pictorial Structures for Object Recognition
International Journal of Computer Vision
Histograms of Oriented Gradients for Human Detection
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
The Bottomn-Left Bin-Packing Heuristic: An Efficient Implementation
IEEE Transactions on Computers
Context Driven Focus of Attention for Object Detection
Attention in Cognitive Systems. Theories and Systems from an Interdisciplinary Viewpoint
Object Detection with Discriminatively Trained Part-Based Models
IEEE Transactions on Pattern Analysis and Machine Intelligence
Quality of service capabilities for hard real-time applications on multi-core processors
Proceedings of the 21st International conference on Real-Time Networks and Systems
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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.