Part I: Basic Method
“Histograms of Oriented Gradients for Human Detection,” N. Dalal and W. Triggs, Proc. IEEE CVPR 2005.
Improvement: Detect the boundary of the object as well: Learning to Detect Natural Image Boundaries Using Local Brightness, Color, and Texture Cues,” D.R. Martin, C.C. Fowlkes, and J. Malik, Proc. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2004.
Part II: Object Detection Method
1. DPM :
“Object Detection Using Discriminatively Trained Part-based Models,” P. Felzenszwalb, R. Girshick, D. McAllester, and D. Ramanan, Proc. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2010.
2. Bags of Features(Global + Local Features):
“Pedestrian Detection in Crowded Scenes” B. Leibe; E. Seemann; B. Schiele, Proc. IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2005
http://ieeexplore.ieee.org.libproxy.sdsu.edu/stamp/stamp.jsp?arnumber=1467359
3. R-CNN(Region proposals + CNN) :
“Rich Feature Hierarchies for Accurate Object Detection and Semantic Segmentation” R. Girshick, J. Donahue, T. Darrell, J. Malik, Proc. CVPR 2014
https://people.eecs.berkeley.edu/~rbg/papers/r-cnn-cvpr.pdf
Faster R-CNN:
“Towards Real-Time Object Detection with Region Proposal Networks” Shaoqing Ren, Kaiming He, Ross Girshick, Jian Sun, Proc. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2016
http://ieeexplore.ieee.org.libproxy.sdsu.edu/stamp/stamp.jsp?arnumber=7485869
4. Multi-filter + Motion, CSS:
“New Features and Insights for Pedestrian Detection” S. Walk, N. Majer, K. Schindler, and B. Schiele, Proc. IEEE Computer Vision and Pattern Recognition, 2010.
http://ieeexplore.ieee.org.libproxy.sdsu.edu/stamp/stamp.jsp?arnumber=5540102
5. CNN:
“Hierarchical Convolutional Features for Visual Tracking” Chao Ma; Jia-Bin Huang; Xiaokang Yang; Ming-Hsuan Yang, Proc. 2015 IEEE International Conference on Computer Vision, 2015
http://ieeexplore.ieee.org.libproxy.sdsu.edu/stamp/stamp.jsp?arnumber=7410709
Part III: Data Set
1. Caltech Pedestrian data set
P. Dolla´r, C. Wojek, B. Schiele, and P. Perona, “Pedestrian Detection: A Benchmark” Proc. IEEE Conf. Computer Vision and Pattern Recognition, 2009.
http://www.vision.caltech.edu/Image_Datasets/CaltechPedestrians/
2. INRIA
N. Dalal and B. Triggs, “Histograms of Oriented Gradients for Human Detection” Proc. IEEE Conf. Computer Vision and Pattern Recognition, 2005.
http://pascal.inrialpes.fr/data/human/
3. VOC-DPM
P. Felzenszwalb, D. McAllester, D. Ramanan, “A Discriminatively Trained, Multiscale, Deformable Part Model” Proc. IEEE Conference on Computer Vision and Pattern Recognition, 2008
https://people.eecs.berkeley.edu/~rbg/latent/
4. Visual Tracker Benchmark
Yi Wu; Jongwoo Lim; Ming-Hsuan Yang “Online Object Tracking: A Benchmark” Proc. IEEE Conference on Computer Vision and Pattern Recognition, 2013
https://sites.google.com/site/trackerbenchmark/benchmarks/v10