A computer vision practical by the Oxford Visual Geometry group, authored by Andrea Vedaldi and Andrew Zisserman.

Start from doc/instructions.html.

The practical consists of four exercises, organized in the following files:

• exercise1.m -- Part 1: Detection fundamentals
• exercise2.m -- Part 2: Multiple scales and learning with an SVM
• exercise3.m -- Part 3: Multiple objects and evaluation
• exercise4.m -- Part 4: Hard negative mining
• exercise5.m -- Part 5: Train your own object detector

The practical runs in MATLAB and uses MatConvNet and VLFeat. This package contains the following MATLAB functions:

• boxinclusion.m: compute the inclusion of bounding boxes.
• boxoverlap.m: compute the overlap of bounding boxes.
• boxsuppress.m: non-maxima box suppression.
• detect.m: sliding window detector.
• detectAtMultipleScales.m: an intermediate example detector.
• evalDetections.m: evaluate detections using the PASCAL VOC criterion.
• evaluateModel.m: evaluate a detector against a database of images.
• extract.m: extract HOG features from bounding boxes.
• loadData.m: load practical data.
• setup.m: setup MATLAB environment.

The practical requires both VLFeat and MatConvNet. VLFeat comes with pre-built binaries, but MatConvNet does not.

1. From Bash, run ./extras/download.sh. This will download the German Street Sign Benchmark data and VLFeat.
2. From MATLAB, run addpath extras ; prepareLabData.m.

• 2014a - Initial edition

Copyright (c) 2011-13 Andrea Vedaldi

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