Download - EECS 274 Computer Vision
EECS 274 Computer Vision
Object detection
Human detection
• 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 DatasetA large annoated dataset with performance evaluation
Performance evaluation
Results (cont’d)
Results (cont’d)
Results (cont’d)
Results (cont’d)
Summary
• 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
An HOG-LBP Human Detector with Partial Occlusion Handling, ICCV 09
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