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Example-Based Object Detection in Images by Components
April 2001 (vol. 23 no. 4)
pp. 349-361

Abstract—In this paper, we present a general example-based framework for detecting objects in static images by components. The technique is demonstrated by developing a system that locates people in cluttered scenes. The system is structured with four distinct example-based detectors that are trained to separately find the four components of the human body: the head, legs, left arm, and right arm. After ensuring that these components are present in the proper geometric configuration, a second example-based classifier combines the results of the component detectors to classify a pattern as either a “person” or a “nonperson.” We call this type of hierarchical architecture, in which learning occurs at multiple stages, an Adaptive Combination of Classifiers (ACC). We present results that show that this system performs significantly better than a similar full-body person detector. This suggests that the improvement in performance is due to the component-based approach and the ACC data classification architecture. The algorithm is also more robust than the full-body person detection method in that it is capable of locating partially occluded views of people and people whose body parts have little contrast with the background.

[1] E. Bauer and R. Kohavi, “An Empirical Comparison of Voting Classification Algorithms: Bagging, Boosting, and Variants,” Machine Learning, 1998.
[2] L. Breiman, “Bagging Predictors,” Machine Learning, vol. 24, pp. 123-140, 1996.
[3] C. Burges, “A Tutorial on Support Vector Machines for Pattern Recognition,” Proc. Data Mining and Knowledge Discovery, U. Fayyad, ed., pp. 1-43, 1998
[4] D. Forsyth and M. Fleck, “Body Plans,” Proc. IEEE Computer Soc. Conf. Computer Vision and Pattern Recognition, pp. 678-683, 1997.
[5] D. Forsyth and M. Fleck, “Finding Naked People,” Int'l J. Computer Vision, 1998. (pending publication.)
[6] Y. Freund and R. Schapire, “Experiments with a New Boosting Algorithm,” Machine Learning: Proc. 13th Nat'l Conf., 1996.
[7] I. Haritaoglu, D. Harwood, and L.S. Davis, “W4 - a Real Time System for Detection and Tracking People and their Parts,” Proc. Third Face and Gesture Recognition Conf., pp. 222-227, 1998.
[8] D. Hogg, “Model-Based Vision: A Program to See a Walking Person,” Image and Vision Computing, vol. 1, no. 1, pp. 5-20, 1983.
[9] T. Joachims, "Text Categorization with Support Vector Machines: Learning with Many Relevant Features," to be published in Proc. 10th European Conf. Machine Learning (ECML), Springer-Verlag, 1998; .
[10] M.K. Leung and Y.H. Yang,“A region-based approach for human body motion analysis,” Pattern Recognition, vol. 20, no. 3, pp. 321-339, 1987.
[11] T. Leung, M. Burl, and P. Perona, "Finding Faces in Cluttered Scenes Using Labeled Random Graph Matching," Int'l Conf. Computer Vision, 1995, pp. 637-644.
[12] S.G. Mallat,“A theory for multiresolution signal decomposition: The wavelet representation,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 11, no. 7, pp. 674-693, 1989.
[13] H. Murase and S.K. Nayar, “Visual Learning and Recognition of 3-D Objects from Appearance,” Int'l J. Computer Vision, vol. 14, pp. 5-24, 1995.
[14] M. Oren, C. Papageorgiou, P. Sinha, E. Osuna, and T. Poggio, Pedestrian Detection Using Wavelet Templates Proc. Computer Vision and Pattern Recognition, pp. 193-199, June 1997.
[15] C.P. Papageorgiou, M. Oren, and T. Poggio, “A General Framework for Object Detection,” Proc. Int'l Conf. Computer Vision, pp. 555-562, 1998.
[16] C. Papageorgiou and T. Poggio, "A Trainable System for Object Detection," Int'l J. Computer Vision, vol. 38, no. 1, June 2000, pp. 15-33.
[17] J. Quinlan, “Bagging, Boosting, and C4.5,” Proc. 13th Nat'l Conf. Artificial Intelligence, 1996.
[18] H. Rowley, S. Baluja, and T. Kanade, "Neural Network-Based Face Detection," IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 20, no. 1, Jan. 1998, pp. 23-38.
[19] H.A. Rowley, S. Baluja, and T. Kanade, “Rotation Invariant Neural Network-Based Face Detection,” Proc. IEEE Conf. Computer Vision and Pattern Recognition, 1998.
[20] L. Shams and J. Spoelstra, “Learning Gabor-Based Features for Face Detection,” Proc. World Congress in Neural Networks, Int'l Neural Network Soc., pp. 15-20, Sept. 1996.
[21] P. Sinha, “Object Recognition via Image Invariants: A Case Study,” Investigative Ophthalmology and Visual Science, vol. 35, pp. 1735-1740, May 1994.
[22] K.-K. Sung and T. Poggio, “Example-Based Learning for View-Based Human Face Detection,” Proc. Image Understanding Workshop, Nov. 1994.
[23] K.K. Sung and T. Poggio, "Example-Based Learning for View-Based Human Face Detection," IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 20, no. 1, pp. 39-50, Jan. 1998.
[24] R. Vaillant, C. Monrocq, and Y. Le Cun, Original Approach for the Localisation of Objects in Images IEEE Proc. Visual Image Signal Process., vol. 141, no. 4, Aug. 1994.
[25] V.N. Vapnik, Statistical Learning Theory, John Wiley&Sons, 1998.
[26] K. Yow and R. Cipolla, “Feature-Based Human Face Detection,” Image and Vision Computing, vol. 15, no. 9, pp. 713-35, Sept. 1997.
[27] A. Yuille, “Deformable Templates for Face Recognition,” J. Cognitive Neuroscience, vol. 3, no. 1, pp. 59-70, 1991.

Index Terms:
Object detection, people detection, pattern recognition, machine learning, components.
Citation:
Anuj Mohan, Constantine Papageorgiou, Tomaso Poggio, "Example-Based Object Detection in Images by Components," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 23, no. 4, pp. 349-361, April 2001, doi:10.1109/34.917571
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