Abstract
We describe an algorithm capable of detecting multiple object instances within a scene in the presence of changes in object viewpoint. Our approach consists of first calculating frequency vectors for discrete feature vector clusters (visual words) within a sliding window as a representation of the image patch. We then classify each patch using an AdaBoost classifier whose weak classifier simply applies a threshold to one visual word’s frequency within the patch. Compared to previous work, our algorithm is simpler yet performs remarkably well on scenes containing many object instances. The method requires relatively few training examples and consumes 2.2 seconds on commodity hardware to process an image of size 640×480. In a test on a challenging car detection problem using a relatively small training set, our implementation dramatically outperforms the detection performance of a standard AdaBoost cascade using Haar-like features.
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References
Viola, P., Jones, M.: Robust real-time face detection. International Journal of Computer Vision 57(2), 137–154 (2004)
Huang, C., Ai, H., Li, Y., Lao, S.: High-performance rotation invariant multiview face detection. IEEE Transactions on Pattern Analysis and Machine Intelligence 29(4), 671–686 (2007)
Mohan, A., Papageorgiou, C., Poggio, T.: Example-based object detection in images by components. IEEE Transactions on Pattern Analysis and Machine Intelligence 23, 349–361 (2001)
Keysers, D., Deselaers, T., Breuel, T.: Optimal geometric matching for patch-based object detection. Electronic Letters on Computer Vision and Image Analysis 6, 44–54 (2007)
Breuel, T.M.: Implementation techniques for geometric branch-and-bound matching methods. Computer Vision and Image Understanding 90, 294 (2003)
Kim, D., Dahyot, R.: Face components detection using SURF descriptors and SVMs. In: International Machine Vision and Image Processing Conference, pp. 51–56 (2008)
Bay, H., Ess, A., Tuytelaars, T., Gool, L.V.: Speeded-up robust features (SURF). Computer Vision and Image Understanding 110(3), 346–359 (2008)
Csurka, G., Dance, C., Fan, L., Willamowski, J., Bray, C.: Visual categorization with bags of keypoints. In: Proceedings of the ECCV International Workshop on Statistical Learning in Computer Vision (2004)
Mikolajczyk, K., Schmid, C.: An affine invariant interest point detector. In: Heyden, A., Sparr, G., Nielsen, M., Johansen, P. (eds.) ECCV 2002. LNCS, vol. 2350, pp. 128–142. Springer, Heidelberg (2002)
Lowe, D.: Object recognition from local scale-invariant features. In: Proceedings of the International Conference on Computer Vision, ICCV (1999)
Das, D., Masur, A., Kobayashi, Y., Kuno, Y.: An integrated method for multiple object detection and localization. In: Proceedings of the 4th International Symposium on Advances in Visual Computing (ISVC), pp. 133–144 (2008)
Zickler, S., Veloso, M.: Detection and localization of multiple objects. In: Proceedings of the IEEE-RAS International Conference on Humanoid Robots, pp. 20–25 (2006)
Zickler, S., Efros, A.: Detection of multiple deformable objects using PCA-SIFT. In: Proceedings of the National Conference on Artificial Intelligence (AAAI), pp. 1127–1133 (2007)
Fergus, R., Perona, P., Zisserman, A.: A sparse object category model for efficient learning and exhaustive recognition. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR) 1, 380–387 (2005)
Hua, G., Brown, M., Winder, S.A.J.: Discriminant embedding for local image descriptors. International Journal of Computer Vision, 1–8 (2007)
Crandall, D., Felzenszwalb, P., Huttenlocher, D.: Spatial priors for part-based recognition using statistical models, pp. 10–17 (2005)
Freund, Y., Schapire, R.: A decision-theoretic generalization of on-line learning and an application to boosting. In: Vitányi, P.M.B. (ed.) EuroCOLT 1995. LNCS, vol. 904, pp. 23–37. Springer, Heidelberg (1995)
OpenCV Community: Open source computer vision library version 1.1, C source code (2008), http://sourceforge.net/projects/opencvlibrary/
Hess, R.: SIFT feature detector, C source code (2006), http://web.engr.oregonstate.edu/~hess/index.html
Bay, H., van Gool, L., Tuytelaars, T.: SURF version 1.0.9 (2006), C source code, http://www.vision.ee.ethz.ch/~surf/
Okuma, K., Taleghani, A., Freitas, N.D., Freitas, O.D., Little, J.J., Lowe, D.G.: A boosted particle filter: Multitarget detection and tracking. In: Pajdla, T., Matas, J(G.) (eds.) ECCV 2004. LNCS, vol. 3021, pp. 28–39. Springer, Heidelberg (2004)
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Tongphu, S., Thongsak, N., Dailey, M.N. (2009). Rapid Detection of Many Object Instances. In: Blanc-Talon, J., Philips, W., Popescu, D., Scheunders, P. (eds) Advanced Concepts for Intelligent Vision Systems. ACIVS 2009. Lecture Notes in Computer Science, vol 5807. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-04697-1_40
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DOI: https://doi.org/10.1007/978-3-642-04697-1_40
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