INRIA Pedestrian Detector
Alexander C. Berg and
Below is a MATLAB/C++ implementation of a pedestrian detector
trained on the INRIA Person dataset. Features are based on
pyramid HOG features and classification is done using the
piecewise linear approximation as described in the paper below:
Classification Using Intersection Kernel Support Vector Machines is efficient.
Subhransu Maji and Alexander C. Berg and Jitendra Malik.
In Proceedings, CVPR 2008, Anchorage, Alaska
- Final Pedestrian Detector
- code is written in MATLAB/C
- PHOG features + IKSVM classification using piecewise linear approximation.
- returns non-max supressed bounding boxes.
- takes about 6-7 seconds on a typical 400x600 image for 10,000
scanning windows. 90% of the time is spent on feature computation.
- INRIA Person dataset benchmarking
Testing is done using the PASCAL criterion which counts a detection
to be correct if the overlap (intersection over union) of the
detected and groundtruth bounding box is greater than 0.5. We run
the detector on the INRIA Test images (both positive and negative). The detection plots
are shown below.
- Benchmarking on precropped positive images can lead to overfitting
because of boundary artifacts. We found that our previous curves on
recall vs. false positives per window were artificially improved
because of this. The new curves are shown below