Abstract
We systematically investigate how geometric constraints can be used for efficient sliding-window object detection. Starting with a general characterization of the space of sliding-window locations that correspond to geometrically valid object detections, we derive a general algorithm for incorporating ground plane constraints directly into the detector computation. Our approach is indifferent to the choice of detection algorithm and can be applied in a wide range of scenarios. In particular, it allows to effortlessly combine multiple different detectors and to automatically compute regions-of-interest for each of them. We demonstrate its potential in a fast CUDA implementation of the HOG detector and show that our algorithm enables a factor 2-4 speed improvement on top of all other optimizations.
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References
Dollar, P., Wojek, C., Schiele, B., Perona, P.: Pedestrian Detection: A Benchmark. In: CVPR (2009)
Gavrila, D., Munder, S.: Multi-Cue Pedestrian Detection and Tracking from a Moving Vehicle. IJCV 73(1), 41–59 (2007)
Leibe, B., Schindler, K., Van Gool, L.: Coupled Object Detection and Tracking from Static Cameras and Moving Vehicles. PAMI 30(10), 1683–1698 (2008)
Ess, A., Leibe, B., Schindler, K., Van Gool, L.: Robust Multi-Person Tracking from a Mobile Platform. PAMI 31(10), 1831–1846 (2009)
Viola, P., Jones, M.: Robust Real-Time Face Detection. IJCVÂ 57(2) (2004)
Wojek, C., Dorkó, G., Schulz, A., Schiele, B.: Sliding-windows for rapid object class localization: A parallel technique. In: Rigoll, G. (ed.) DAGM 2008. LNCS, vol. 5096, pp. 71–81. Springer, Heidelberg (2008)
Prisacariu, V., Reid, I.: fastHOG – a Real-Time GPU Implementation of HOG. Technical Report 2310/09, Dept. of Eng. Sc., Univ. of Oxford (2009)
Torralba, A., Murphy, K., Freeman, W.: Sharing Features: Efficient Boosting Procedures for Multiclass Object Detection. In: CVPR (2004)
Vedaldi, A., Gulshan, V., Varma, M., Zisserman, A.: Multiple kernels for object detection. In: ICCV (2009)
Felzenszwalb, P., Girshick, R., McAllester, D.: Cascade Object Detection with Deformable Part Models. In: CVPR (2010)
Dollar, P., Tu, Z., Perona, P., Belongie, S.: Integral Channel Features. In: BMVC (2009)
Dollar, P., Belongie, S., Perona, P.: The Fastest Pedestrian Detector in the West. In: BMVC (2010)
Lampert, C., Blaschko, M., Hofmann, T.: Efficient Subwindow Search: A Branch and Bound Framework for Object Localization. PAMI 31(12), 2129–2142 (2009)
Hoiem, D., Efros, A., Hebert, M.: Putting Objects Into Perspective. In: CVPR (2006)
Geronimo, D., Sappa, A., Ponsa, D., Lopez, A.: 2D-3D-based On-Board Pedestrian Detection System. CVIU 114(5), 583–595 (2010)
Choi, W., Savarese, S.: Multiple target tracking in world coordinate with single, minimally calibrated camera. In: Daniilidis, K., Maragos, P., Paragios, N. (eds.) ECCV 2010. LNCS, vol. 6314, pp. 553–567. Springer, Heidelberg (2010)
Park, D., Ramanan, D., Fowlkes, C.: Multiresolution models for object detection. In: Daniilidis, K., Maragos, P., Paragios, N. (eds.) ECCV 2010. LNCS, vol. 6314, pp. 241–254. Springer, Heidelberg (2010)
Felzenszwalb, P., McAllester, D., Ramanan, D.: A Discriminatively Trained, Multiscale, Deformable Part Model. In: CVPR (2008)
Dalal, N., Triggs, B.: Histograms of Oriented Gradients for Human Detection. In: CVPR (2005)
Breitenstein, M., Sommerlade, E., Leibe, B., Van Gool, L., Reid, I.: Probabilistic Parameter Selection for Learning Scene Structure from Video. In: BMVC (2008)
Bao, Y., Sun, M., Savarese, S.: Toward Coherent Object Detection And Scene Layout Understanding. In: CVPR (2010)
Sun, M., Bao, Y., Savarese, S.: Object Detection with Geometrical Context Feedback Loop. In: BMVC (2010)
Bombini, L., Cerri, P., Grisleri, P., Scaffardi, S., Zani, P.: An Evaluation of Monocular Image Stabilization Algorithms for Automotive Applications. Intel. Transp. Syst (2006)
Schneiderman, H.: Feature-Centric Evaluation for Efficient Cascaded Object Detection. In: CVPR (2004)
Lehmann, A., Leibe, B., Van Gool, L.: Feature-Centric Efficient Subwindow Search. In: ICCV (2009)
Hartley, R., Zisserman, A.: Multiple view geometry in computer vision. Cambridge University Press, Cambridge (2000)
Everingham, M., et al.: (34 authors): The 2005 PASCAL Visual Object Classes Challenge. In: Quiñonero-Candela, J., Dagan, I., Magnini, B., d’Alché-Buc, F. (eds.) MLCW 2005. LNCS (LNAI), vol. 3944, pp. 117–176. Springer, Heidelberg (2006)
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Sudowe, P., Leibe, B. (2011). Efficient Use of Geometric Constraints for Sliding-Window Object Detection in Video. In: Crowley, J.L., Draper, B.A., Thonnat, M. (eds) Computer Vision Systems. ICVS 2011. Lecture Notes in Computer Science, vol 6962. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-23968-7_2
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DOI: https://doi.org/10.1007/978-3-642-23968-7_2
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