Abstract
This paper presents a fast object class localization framework implemented on a data parallel architecture currently available in recent computers. Our case study, the implementation of Histograms of Oriented Gradients (HOG) descriptors, shows that just by using this recent programming model we can easily speed up an original CPU-only implementation by a factor of 34, making it unnecessary to use early rejection cascades that sacrifice classification performance, even in real-time conditions. Using recent techniques to program the Graphics Processing Unit (GPU) allow our method to scale up to the latest, as well as to future improvements of the hardware.
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References
Nowak, E., Jurie, F., Triggs, B.: Sampling strategies for bag-of-features image classification. In: Leonardis, A., Bischof, H., Pinz, A. (eds.) ECCV 2006. LNCS, vol. 3954. Springer, Heidelberg (2006)
Tuytelaars, T., Schmid, C.: Vector quantizing feature space with a regular lattice. In: ICCV (October 2007)
Viola, P.A., Jones, M.J.: Robust real-time face detection. IJCV 57(2), 137–154 (2004)
Papageorgiou, C., Poggio, T.: A trainable system for object detection. IJCV 38(1), 15–33 (2000)
Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: CVPR, pp. 886–893 (2005)
Shashua, A., Gdalyahu, Y., Hayun, G.: Pedestrian detection for driving assistance systems: Single-frame classification and system level performance. In: International Symposium on Intelligent Vehicles, pp. 1–6 (2004)
Laptev, I.: Improvements of object detection using boosted histograms. In: BMVC, vol. III, pp. 949 (September 2006)
Tuzel, O., Porikli, F., Meer, P.: Human detection via classification on Riemannian manifolds. In: CVPR (June 2007)
Sabzmeydani, P., Mori, G.: Detecting pedestrians by learning shapelet features. In: CVPR (June 2007)
Munder, S., Gavrila, D.M.: An experimental study on pedestrian classification. PAMI 28(11), 1863–1868 (2006)
Gavrila, D.M., Philomin, V.: Real-time object detection for smart vehicles. In: ICCV, pp. 87–93 (1999)
Zhu, Q., Avidan, S., Yeh, M., Cheng, K.: Fast human detection using a cascade of histograms of oriented gradients. In: CVPR (June 2006)
Zhang, W., Zelinsky, G., Samaras, D.: Real-time accurate object detection using multiple resolutions. In: ICCV (October 2007)
Everingham, M., Zisserman, A., Williams, C., van Gool, L.: PASCAL visual object classes challenge results (2006)
Lowe, D.G.: Distinctive image features from scale-invariant keypoints. IJCV 60(2), 91–110 (2004)
OpenVIDIA: GPU accelerated CV library, http://openvidia.sourceforge.net/
Björkman, M.: CUDA implementation of SIFT (2007)
NVIDIA: NVIDIA CUDA SDK code samples
Comaniciu, D.: An algorithm for data-driven bandwidth selection. PAMI 25(2), 281–288 (2003)
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Wojek, C., Dorkó, G., Schulz, A., Schiele, B. (2008). Sliding-Windows for Rapid Object Class Localization: A Parallel Technique. In: Rigoll, G. (eds) Pattern Recognition. DAGM 2008. Lecture Notes in Computer Science, vol 5096. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-69321-5_8
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DOI: https://doi.org/10.1007/978-3-540-69321-5_8
Publisher Name: Springer, Berlin, Heidelberg
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