Download EECS 274 Computer Vision

EECS 274 Computer Vision
Object detection
Human detection
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HOG features
Cue integration
Ensemble of classifiers
ROC curve
• Reading: Assigned papers
Human detection with HOG
• Histogram of oriented
gradients
• Using local gradients to
represent positive and
negative examples
Histogram of oriented gradients
HOG descriptors
Results with MIT dataset
Results with INRIA dataset
Parameter sweeping
Block/cell size
Results
Observations
• No gradient smoothing with [-1,0,1]
derivative filter
• Use gradient magnitude (no thresholding)
• Orientation voting into fine bins
• Spatial voting into coarser bins
• Strong local normalization
• Overlapping normalization blocks
Cal Tech Pedestrian Dataset
A large annoated dataset with performance evaluation
Performance evaluation
Results (cont’d)
Results (cont’d)
Results (cont’d)
Results (cont’d)
Summary
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HOG, MultiFtr, FtrMine outperform others
VJ and Shaplet perform poorly
LatSvm trained on PASCAL dataset
HOG poerforms best on near, unoccluded
pedestrians
• MultiFtr ties or outperforms HOG on
difficult cases
• Much room for imporvment
Daimler dataset
• Recent survey in PAMI 09
• Observation
– HOG/linSVM at higher image resolution
performs well, with lower processing speed)
– Wavelet-based Adaboost cascade at lower
image resolution performs well, with higher
processing speed
Neural network with receptive fields
Results
Cue integration
Multi-cue pedestrian detection and tracking from a moving vehicle, IJCV 06
Classifier ensemble
• Cascade of boosted classifiers
• Variable-size blocks: 12 x 12, 64 x 128, etc. 
5031 blocks in 64 x 128 image patch
Fast human detection using a cascade of histograms of oriented
gradients, CVPR 06
Classifier ensemble
An HOG-LBP Human Detector with Partial Occlusion Handling, ICCV 09
Convert holistic classifier to local-classifier ensemble
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An HOG-LBP Human Detector with Partial Occlusion Handling, ICCV 09