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Detecting Pedestrians by Learning Shapelet Features

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1 Detecting Pedestrians by Learning Shapelet Features
Payam Sabzmeydani and Greg Mori Vision and Media Lab School of Computing Science Simon Fraser University The main problem that I worked on during my masters was pedestrian detection and today I’ll talk about detecting pedestrians in still images using learned shape features

2 Problem Given a still image, we want to find and locate the pedestrians in the image Clothing (color, appearance) Body pose Applications: Automated surveillance systems Image search and retrieval Robotics Intelligent vehicles

3 Problem

4 Problem Classification-based detection Different cues
Classify a window as pedestrian or non-pedestrian Search exhaustively the scale-space image Different cues Wavelet coefficients (Mohan et al., PAMI 2001) Oriented gradients (Dalal and Triggs, CVPR 2005) SIFT features (Leibe et al., CVPR 2005) Edgelet features (Wu and Nevatia, ICCV 2005) “Shapelet features” (Sabzmeydani and Mori, CVPR 2007)

5 Datasets MIT : Standing pose, simple background, no occlusion
INRIA : Standing pose, complex background, partial occlusions

6 Previous Work Dalal & Triggs (CVPR 2005) HOG features + SVM

7 Previous Work Wu & Nevatia (ICCV 2005)
Edgelet features: short line and curve segments AdaBoost

8 Our Method Compute low-level gradient features
Oriented filter responses Learn mid-level features for detecting pedestrians “Shapelet features” Build final classifier from shapelet features

9 Low-level Features Filter responses
Image gradient in different directions

10 Low-level Features Smoothed gradient responses in different directions

11 Shapelet Features A weighted set of low-level gradient features inside a sub-window of the detection window Characteristics Simple and low-dimensional Learned exclusively for our object classes Highly discriminative Local effective area : useful to model separate parts instead of the whole body

12 Shapelet Features

13 Learning Shapelet Features
Learned using AdaBoost (Viola and Jones, 2001) Extract low-level features in sub-window Select subset of features using AdaBoost Find those which discriminate between pedestrian and background classes

14 AdaBoost Algorithm W w

15 Low-level features as weak classifiers
Each low-level feature can provide us many weak classifiers: AdaBoost will combine weak classifiers to form a better classifier:

16 Shapelet features Train classifiers in sub-windows
Use the output of a classifier as the shapelet feature response:

17 Shapelet Features

18 Shapelet Features

19 Final Classifier Take all shapelet features
Learned at many sub-windows of detection window Run AdaBoost again to select weighted subset of shapelet features for final classifier

20 Final Classifier A weighted sub-set of mid-level features

21 Shapelet Feature Size Small, Medium, and Large features
Capture different scales of information

22 Normalization Why normalize? How to normalize?
Different lighting, shadows, different contrast, … How to normalize? Per shapelet feature : L2-norm

23 Normalization

24 Results on INRIA Dataset

25 Error examples Most non-pedestrian-like pedestrians (false negatives)
Most pedestrian-like non-pedestrians (false positives)

26 Future work Detecting other objects Use image context or segmentation
Pyramid of features


28 References N. Dalal and B. Triggs. “Histograms of oriented gradients for human detection”. CVPR 2005. B. Wu and R. Nevatia. “Detection of multiple, partially occluded humans in a single image by bayesian combination of edgelet part detectors”. ICCV 2005. P. Viola and M. Jones. “Rapid object detection using a boosted cascade of simple features”. SCTV 2001.

29 Problem

30 Problem

31 Bootstrapping

32 Mid-level Features

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