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Paper-by-paper results make it easy to miss the forest for the trees.We analyse the remarkable progress of the last decade by discussing the main ideas explored in the 40+ detectors currently present in the Caltech pedestrian detection benchmark.
Detecting pedestrians using patterns of motion and appearance
Viola, P., Jones, M., Snow, D.: · 2003
Earlier work this paper cites.
Robust real-time face detection
Viola, P., Jones, M.: · 2004
Earlier work this paper cites.
Histograms of oriented gradients for human detection
Dalal, N., Triggs, B.: · 2005
Earlier work this paper cites.
Detecting pedestrians by learning shapelet features
Sabzmeydani, P., Mori, G.: · 2007
Earlier work this paper cites.
Feature mining for image classification
P. Dollár, Z. Tu, H.T., Belongie, S.: · 2007
Earlier work this paper cites.
A mobile vision system for robust multi-person tracking
Ess, A., Leibe, B., Schindler, K., Van Gool, L.: · 2008
Earlier work this paper cites.
A pose-invariant descriptor for human detection and segmentation
Lin, Z., Davis, L.: · 2008
Earlier work this paper cites.
A discriminatively trained, multiscale, deformable part model
Felzenszwalb, P., McAllester, D., Ramanan, D.: · 2008
Earlier work this paper cites.
Classification using intersection kernel support vector machines is efficient
Maji, S., Berg, A., Malik, J.: · 2008
Earlier work this paper cites.
A performance evaluation of single and multi-feature people detection
Wojek, C., Schiele, B.: · 2008
Earlier work this paper cites.
Multi-cue onboard pedestrian detection
Wojek, C., Walk, S., Schiele, B.: · 2009
Earlier work this paper cites.
Monocular pedestrian detection: Survey and experiments
Enzweiler, M., Gavrila, D.M.: · 2009
Earlier work this paper cites.
Dense stereo-based roi generation for pedestrian detection
Keller, C., Fernandez, D., Gavrila, D.: · 2009
Earlier work this paper cites.
Pedestrian detection: A benchmark
Dollar, P., Wojek, C., Schiele, B., Perona, P.: · 2009
Earlier work this paper cites.
An hog-lbp human detector with partial occlusion handling
Wang, X., Han, X., Yan, S.: · 2009
Earlier work this paper cites.
Human detection using partial least squares analysis
Schwartz, W., Kembhavi, A., Harwood, D., Davis, L.S.: · 2009
Earlier work this paper cites.
Integral channel features
Dollár, P., Tu, Z., Perona, P., Belongie, S.: · 2009
Earlier work this paper cites.
Robust multi-person tracking from a mobile platform
Ess, A., Leibe, B., Schindler, K., Van Gool, L.: · 2009
Earlier work this paper cites.
Object detection with discriminatively trained part-based models
Felzenszwalb, P., Girshick, R., McAllester, D., Ramanan, D.: · 2010
Earlier work this paper cites.
New features and insights for pedestrian detection
Walk, S., Majer, N., Schindler, K., Schiele, B.: · 2010
Earlier work this paper cites.
Part-based feature synthesis for human detection
Bar-Hillel, A., Levi, D., Krupka, E., Goldberg, C.: · 2010
Cited alongside, same era.
The fastest pedestrian detector in the west
Dollár, P., Belongie, S., Perona, P.: · 2010
Cited alongside, same era.
Multiresolution models for object detection
Park, D., Ramanan, D., Fowlkes, C.: · 2010
Cited alongside, same era.
Auto-context and its application to high-level vision tasks and 3d brain image segmentation
Tu, Z., Bai, X.: · 2010
Cited alongside, same era.
Pedestrian detection: An evaluation of the state of the art
Dollár, P., Wojek, C., Schiele, B., Perona, P.: · 2011
Cited alongside, same era.
Improving object localization using macrofeature layout selection
Nam, W., Han, B., Han, J.: · 2011
Cited alongside, same era.
Detection evolution with multi-order contextual co-occurrence
Chen, G., Ding, Y., Xiao, J., Han, T.X.: · 2013
Later among the works it cites.
Multi-stage contextual deep learning for pedestrian detection
Zeng, X., Ouyang, W., Wang, X.: · 2013
Later among the works it cites.
Robust multi-resolution pedestrian detection in traffic scenes
Yan, J., Zhang, X., Lei, Z., Liao, S., Li, S.Z.: · 2013
Later among the works it cites.
Joint deep learning for pedestrian detection
Ouyang, W., Wang, X.: · 2013
Later among the works it cites.
Exploring weak stabilization for motion feature extraction
Park, D., Zitnick, C.L., Ramanan, D., Dollár, P.: · 2013
Later among the works it cites.
Sketch tokens: A learned mid-level representation for contour and object detection
Lim, J., Zitnick, C.L., Dollár, P.: · 2013
Later among the works it cites.
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The benefits of dense stereo for pedestrian detection
Keller, C.G., Enzweiler, M., Rohrbach, M., Fernandez Llorca, D., Schnorr, C., Gavrila, D.M.: · 2011
Cited alongside, same era.
A multilevel mixture-of-experts framework for pedestrian classification
Enzweiler, M., Gavrila, D.: · 2011
Cited alongside, same era.
Are we ready for autonomous driving? the kitti vision benchmark suite
Geiger, A., Lenz, P., Urtasun, R.: · 2012
Cited alongside, same era.
Crosstalk cascades for frame-rate pedestrian detection
Dollár, P., Appel, R., Kienzle, W.: · 2012
Cited alongside, same era.
A discriminative deep model for pedestrian detection with occlusion handling
Ouyang, W., Wang, X.: · 2012
Cited alongside, same era.
Pedestrian detection at 100 frames per second
Benenson, R., Mathias, M., Timofte, R., Van Gool, L.: · 2012
Cited alongside, same era.
Fast feature pyramids for object detection
Dollár, P., Appel, R., Belongie, S., Perona, P.: · 2014
Closest in time.
Word channel based multiscale pedestrian detection without image resizing and using only one classifier
Costea, A.D., Nedevschi, S.: · 2014
Closest in time.
Switchable deep network for pedestrian detection
Luo, P., Tian, Y., Wang, X., Tang, X.: · 2014
Closest in time.
Informed haar-like features improve pedestrian detection
Zhang, S., Bauckhage, C., Cremers, A.B.: · 2014
Closest in time.
Pedestrian detection combining rgb and dense lidar data
Premebida, C., Carreira, J., Batista, J., Nunes, U.: · 2014
Closest in time.
The fastest deformable part model for object detection
Yan, J., Lei, Z., Wen, L., Li, S.Z.: · 2014
Closest in time.
Detecting objects using deformation dictionaries
Hariharan, B., Zitnick, C.L., Dollár, P.: · 2014
Closest in time.
Using a deformation field model for localizing faces and facial points under weak supervision
Pedersoli, M., Tuytelaars, T., Gool, L.V.: · 2014
Closest in time.
Rich feature hierarchies for accurate object detection and semantic segmentation
Girshick, R., Donahue, J., Darrell, T., Malik, J.: · 2014
Closest in time.
Overfeat: Integrated recognition, localization and detection using convolutional networks
Sermanet, P., Eigen, D., Zhang, X., Mathieu, M., Fergus, R., LeCun, Y.: · 2014
Closest in time.
Recurrent convolutional neural networks for scene labeling
Pinheiro, P., Collobert, R.: · 2014
Closest in time.
From generic to specific deep representations for visual recognition
Azizpour, H., Razavian, A.S., Sullivan, J., Maki, A., Carlsson, S.: · 2014
Closest in time.
Strengthening the effectiveness of pedestrian detection with spatially pooled features
Paisitkriangkrai, S., Shen, C., van den Hengel, A.: · 2014
Closest in time.
Local decorrelation for improved detection
Nam, W., Dollár, P., Han, J.H.: · 2014
Closest in time.