Abstract
Automatic detection systems do not perform as well as human observers, even on simple detection tasks. A potential solution to this problem is training vision systems on appropriate regions of interests (ROIs), in contrast to training on predefined and arbitrarily selected regions. Here we focus on detecting pedestrians in static scenes. Our aim is to answer the following question: Can automatic vision systems for pedestrian detection be improved by training them on perceptually-defined ROIs?
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Birchfield, S.: Elliptical head tracking using intensity gradients and color histograms. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 232–237 (June 1998)
La Cascia, M., Sclaroff, S.: Fast, reliable head tracking under varying illumination: An approach based on registration of texture-mapped 3d models. IEEE Trans. on Pattern Analysis and Machine Intelligence 22(4), 322–336 (2000)
Gosselin, F., Schyns, P.G.: Bubbles: a technique to reveal the use of information in recognition tasks. In: Vision Research, pp. 2261–2271 (2001)
Hjelmas, E., Low, B.K.: Face detection: A survey. Computer Vision and Image Understanding 83(3), 236–274 (2001)
Intel. Intel Open Source Computer Vision Library, b4.0 (August. 2004), http://www.intel.com/research/mrl/research/opencv
Jones, M.J., Rehg, J.M.: Statistical color models with application to skin detection. Technical Report Series CRL 98/11, Cambridge Research Laboratory (December 1998)
Leibe, B., Seemann, E., Schiele, B.: Pedestrian detection in crowded scenes. In: Proceedings of the CVPR 2005 (2005)
Lienhart, R., Kuranov, A., Pisarevsky, V.: Empirical analysis of detection cascades of boosted classifiers for rapid object detection. In: Michaelis, B., Krell, G. (eds.) DAGM 2003. LNCS, vol. 2781, pp. 297–304. Springer, Heidelberg (2003)
Lowe, D.: Distinctive image features from scale-invariant keypoints. International Journal of Comnputer Vision 60(2), 91–110 (2004)
Mikolajczyk, K., Schmid, C., Zisserman, A.: Human detection based on a probabilistic assembly of robust part detectors. In: European Conference on Computer Vision, vol. I, pp. 69–81 (2004)
Papageorgiou, C., Oren, M., Poggio, T.: A general framework for object detection. In: Proceedings of the International Conference on Computer Vision, pp. 555–562 (1998)
Papageorgiou, C., Poggio, T.: A trainable system for object detection. International Journal of Computer Vision 38(1) (2000)
Viola, P., Jones, M.J.: Rapid object detection using a boosted cascade of simple features. In: Computer Vision and Pattern Recognition, pp. 511–518 (2001)
Viola, P., Jones, M.J.: Robust real-time face detection. International Journal of Computer Vision 57(2), 151–173 (2004)
Viola, P., Jones, M.J., Snow, D.: Detecting pedestrians using patterns of motion and appearance. In: Proc. of the International Conference on Computer Vision, vol. 2, pp. 734–741 (October 2003)
Vuong, Q.C., Hof, A., Bülthoff, H.H., Thornton, I.M.: An advantage for detecting human targets in dynamic versus static composite stimuli. In: 4th annual meeting of the Vision Sciences Society (2004)
Yang, M.-H., Kriegman, D., Ahuja, N.: Detecting faces in images: A survey. Transactions on Pattern Analysis and Machine Intelligence 24(1), 34–58 (2002)
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Castrillón-Santana, M., Vuong, Q.C. (2006). Combining Human Perception and Geometric Restrictions for Automatic Pedestrian Detection. In: Marín, R., Onaindía, E., Bugarín, A., Santos, J. (eds) Current Topics in Artificial Intelligence. CAEPIA 2005. Lecture Notes in Computer Science(), vol 4177. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11881216_18
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DOI: https://doi.org/10.1007/11881216_18
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