I. The history of recognition
II. The process of object recognition
http://vision.stanford.edu/teaching/cs131_fall1617/lectures/lecture17_intro_objrecog_cs131.pdf
Step 1. Image features
Step 2. Learning
A. Classification
There are many methods to choose from:
• K-nearest neighbor
• SVM
• Neural networks
• Naïve Bayes
• Bayesian network
• Logic regression
• Randomized Forests
• Boosted Decision Trees
• RBMs
• Etc.
III. Typical Method
A. Bags of Features
http://www.cs.cornell.edu/courses/cs4670/2015sp/lectures/lec35_reco3_web.pdf
(1) Origin
Texture recognition:
• Texture is characterized by the repetition of basic elements or textons
• For stochastic textures, the identity of the textons, not their spatial arrangement, matters
Julesz, 1981; Cula & Dana, 2001; Leung & Malik 2001; Mori, Belongie & Malik, 2001; Schmid 2001; Varma & Zisserman, 2002, 2003; Lazebnik, Schmid & Ponce, 2003
Bag-of-words models:
Orderless document representation: frequencies of words from a dictionary. Salton & McGill (1983)
(2) Outline
Step 2: Using K-means clustering
Step 4: K nearest neighbors
B. CNN(Convolutional Neural Network)
http://cs231n.github.io/convolutional-networks/#overview
IV. Data Set
A. Pedestrian
http://ieeexplore.ieee.org.libproxy.sdsu.edu/stamp/stamp.jsp?arnumber=5975165&tag=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. - ETH:
A. Ess, B. Leibe, and L. Van Gool, “Depth and Appearance for Mobile Scene Analysis,” Proc. IEEE Int’l Conf. Computer Vision, 2007. - TUB-Brussels
C. Wojek, S. Walk, and B. Schiele, “Multi-Cue Onboard Pedestrian Detection,” Proc. IEEE Conf. Computer Vision and Pattern Recognition, 2009 - Daimler
M. Enzweiler and D.M. Gavrila, “Monocular Pedestrian Detection: Survey and Experiments,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 31, no. 12, pp. 2179- 2195, Dec. 2009. - INRIA
N. Dalal and B. Triggs, “Histograms of Oriented Gradients for Human Detection,” Proc. IEEE Conf. Computer Vision and Pattern Recognition, 2005.
B. Vehicle