Abstract
Over the years a number of powerful people detectors have been proposed. While it is standard to test complete detectors on publicly available datasets, it is often unclear how the different components (e.g. features and classifiers) of the respective detectors compare. Therefore, this paper contributes a systematic comparison of the most prominent and successful people detectors. Based on this evaluation we also propose a new detector that outperforms the state-of-art on the INRIA person dataset by combining multiple features.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Papageorgiou, C., Poggio, T.: A trainable system for object detection. IJCV 38(1), 15–33 (2000)
Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: CVPR, pp. 886–893 (2005)
Seemann, E., Leibe, B., Schiele, B.: Multi-aspect detection of articulated objects. In: CVPR, pp. 1582–1588 (2006)
Forsyth, D., Fleck, M.: Body plans. In: CVPR (1997)
Wu, B., Nevatia, R.: Detection of multiple, partially occluded humans in a single image by bayesian combination of edgelet part detectors. In: ICCV (2005)
Felzenszwalb, P., Huttenlocher, D.: Efficient matching of pictorial structures. In: CVPR (2000)
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. 3024, pp. 69–81. Springer, Heidelberg (2004)
Seemann, E., Leibe, B., Mikolajczyk, K., Schiele, B.: An evaluation of local shape-based features for pedestrian detection. In: BMVC (2005)
Munder, S., Gavrila, D.M.: An experimental study on pedestrian classification. PAMI 28(11), 1863–1868 (2006)
Viola, P.A., Jones, M.J.: Robust real-time face detection. IJCV 57(2), 137–154 (2004)
Gavrila, D.: Multi-feature hierarchical template matching using distance transforms. In: Proceedings of the International Conference on Pattern Recognition, vol. 1, pp. 439–444 (1998)
Shashua, A., Gdalyahu, Y., Hayun, G.: Pedestrian detection for driving assistance systems: Single-frame classification and system level performance. In: International Symposium on Intelligent Vehicles, pp. 1–6 (2004)
Sabzmeydani, P., Mori, G.: Detecting pedestrians by learning shapelet features. In: CVPR (2007)
Tuzel, O., Porikli, F., Meer, P.: Human detection via classification on Riemannian manifolds. In: CVPR (2007)
Belongie, S., Malik, J., Puzicha, J.: Shape matching and object recognition using shape contexts. PAMI 24(4), 509–522 (2002)
Leibe, B., Seemann, E., Schiele, B.: Pedestrian detection in crowded scenes. In: CVPR, pp. 878–885 (2005)
Mikolajczyk, K., Schmid, C.: A performance evaluation of local descriptors. PAMI 27(10), 1615–1630 (2005)
Joachims, T.: Making large–scale SVM learning practical. In: Schölkopf, B., Burges, C.J.C., Smola, A.J. (eds.) Advances in Kernel Methods — Support Vector Learning, pp. 169–184. MIT Press, Cambridge (1999)
Comaniciu, D.: An algorithm for data-driven bandwidth selection. PAMI 25(2), 281–288 (2003)
Everingham, M., Zisserman, A., Williams, C., van Gool, L.: The PASCAL visual object classes challenge (VOC 2006) results. Technical report (2006)
Levi, K., Weiss, Y.: Learning object detection from a small number of examples: The importance of good features. In: CVPR, vol. II, pp. 53–60 (2004)
Author information
Authors and Affiliations
Editor information
Rights and permissions
Copyright information
© 2008 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Wojek, C., Schiele, B. (2008). A Performance Evaluation of Single and Multi-feature People Detection. In: Rigoll, G. (eds) Pattern Recognition. DAGM 2008. Lecture Notes in Computer Science, vol 5096. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-69321-5_9
Download citation
DOI: https://doi.org/10.1007/978-3-540-69321-5_9
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-69320-8
Online ISBN: 978-3-540-69321-5
eBook Packages: Computer ScienceComputer Science (R0)