Robust Human Detection under Occlusion by Integrating Face and Person Detectors
Conference paper
Abstract
Human detection under occlusion is a challenging problem in computer vision. We address this problem through a framework which integrates face detection and person detection. We first investigate how the response of a face detector is correlated with the response of a person detector. From these observations, we formulate hypotheses that capture the intuitive feedback between the responses of face and person detectors and use it to verify if the individual detectors’ outputs are true or false. We illustrate the performance of our integration framework on challenging images that have considerable amount of occlusion, and demonstrate its advantages over individual face and person detectors.
Keywords
False Alarm Partial Little Square Face Detector Detection Window Probability Interval
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References
- 1.Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: CVPR, vol. 1, pp. 886–893 (2005)Google Scholar
- 2.Tuzel, O., Porikli, F., Meer, P.: Human detection via classification on riemannian manifolds. In: CVPR, pp. 1–8 (2007)Google Scholar
- 3.Wu, B., Nevatia, R.: Optimizing discrimination-efficiency tradeoff in integrating heterogeneous local features for object detection. In: CVPR, pp. 1–8 (2008)Google Scholar
- 4.Maji, S., Berg, A., Malik, J.: Classification using intersection kernel support vector machines is efficient. In: CVPR, pp. 1–8 (2008)Google Scholar
- 5.Wu, B., Nevatia, R.: Detection of multiple, partially occluded humans in a single image by bayesian combination of edgelet part detectors. In: ICCV, pp. 90–97 (2005)Google Scholar
- 6.Mikolajczyk, K., Schmid, C., Zisserman, A.: Human detection based on a probabilistic assembly of robust part detectors. In: Pajdla, T., Matas, J(G.) (eds.) ECCV 2004. LNCS, vol. 3021, pp. 69–82. Springer, Heidelberg (2004)Google Scholar
- 7.Shet, V., Neumann, J., Ramesh, V., Davis, L.: Bilattice-based logical reasoning for human detection. In: CVPR, pp. 1–8 (2007)Google Scholar
- 8.Yang, M.H., Kriegman, D.J., Ahuja, N.: Detecting Faces in Images: A Survey. IEEE Transactions on Pattern Analysis and Machine Intelligence, 34–58 (2002)Google Scholar
- 9.Viola, P., Jones, M.: Robust Real-Time Face Detection. International Journal of Computer Vision 57, 137–154 (2004)Google Scholar
- 10.Rowley, H., Baluja, S., Kanade, T.: Neural network-based face detection. IEEE Transactions on Pattern Analysis and Machine Intelligence 20, 23–38 (1998)Google Scholar
- 11.Heisele, B., Serre, T., Poggio, T.: A Component-based Framework for Face Detection and Identification. IJCV 74, 167–181 (2007)Google Scholar
- 12.Moon, H., Chellappa, R., Rosenfeld, A.: Optimal edge-based shape detection. IEEE Transactions on Image Processing 11, 1209–1227 (2002)Google Scholar
- 13.Hsu, R., Abdel-Mottaleb, M., Jain, A.: Face detection in color images. IEEE Transactions on Pattern Analysis and Machine Intelligence 24, 696–706 (2002)Google Scholar
- 14.Belhumeur, P., Hespanha, J., Kriegman, D.: Eigenfaces vs. Fisherfaces: Recognition Using Class Specific Linear Projection. PAMI, 711–720 (1997)Google Scholar
- 15.Osuna, E., Freund, R., Girosit, F.: Training support vector machines: an application to face detection. In: CVPR, pp. 130–136 (1997)Google Scholar
- 16.Haralick, R., Shanmugam, K., Dinstein, I.: Texture features for image classification. IEEE Transactions on Systems, Man, and Cybernetics 3 (1973)Google Scholar
- 17.Wold, H.: Partial least squares. In: Kotz, S., Johnson, N.L. (eds.) Encyclopedia of Statistical Sciences, pp. 581–591. Wiley, New York (1985)Google Scholar
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