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scales, and then run the same classifier again and again on these resized inputs. The second is
presented in the improved version, by running multiple classifiers that are trained on different
scales on the same input.

The second approach will be the faster alternative, unfortunately, I am unable to obtain a
reasonable recall / precision with the second approach.

Running in the first mode, on a computer with Core i7 3770K, with INRIA 2008 test set, the
figures are:

	real	2m19.18s user	 2m16.30s sys	  0m2.79s

It is still slower than HOG, but faster than DPM implementation in libccv.

Accuracy-wise:

The pedestrian.icf model provided in ./samples are trained with INRIA 2008 training dataset,
but with additional 7542 negative samples collected from VOC2011. The model is trained at size
31x74, with 6px margins on each side.

The provided model is then tested with INRIA 2008 test dataset, if bounding boxes overlap is
greater than 0.5 of the bigger bounding boxes, it is a true positive.  The validation script
is available at ./bin/icfvldtr.rb.

	76.23% (52)

Which has roughly the same recall as DPM implementation provided in ccv, with roughly the same
false alarms too.

How to train my own detector?
-----------------------------

ccv provides utilities to train your own object models. Specifically, for ICF, these utilities
are available at ./bin/icfcreate and ./bin/icfoptimize.

	./icfcreate --help

Will show you the parameters that ccv supports when training an object model.

If you have libdispatch installed and properly enabled on your machine, ccv will utilize all
your CPU cores to speed up the training process.

The INRIA pedestrian dataset can be downloaded from:

	http://pascal.inrialpes.fr/data/human/

The annotation format is substantially different from what ccv requires, I use this simple
script to extract annotations from INRIA dataset:

	https://gist.github.com/liuliu/6349801

You also want to have a collection of background (none pedestrian) files, I combined data from
both INRIA and VOC2011 to generates that list:

	find ../data/negs/*.jpg > no-pedestrian.txt

After all these ready, and have a PC with enough computational power:

	./icfcreate --positive-list pedestrian.icf_samples --background-list no-pedestrian.txt --validate-list pedestrian.icf_test --negative-count 10000 --positive-count 10000 --feature-size 50000 --weak-classifier-count 2000 --size 30x90 --margin 10,10,10...

The classifier cascade will be bootstrapping 3 times, pooling from 50,000 features, and the
final boosted classifier will have 2,000 weak classifier. On the PC that I am running (with
SSD / hard-drive hybrid (through flashcache), 32GiB memory and Core i7 3770K), it takes a day
to finish training one classifier. At minimal, you should have about 16GB available memory to
get the program finish running.

The final-cascade file in your working directory is the classifier model file that you can
use. Using ./bin/icfoptimize, you should be able to set proper soft cascading thresholds for
the classifier to speed up detection:

	./icfoptimize --positive-list pedestrian.icf_test --classifier-cascade icf-data/final-cascade --acceptance 0.7 --base-dir ../data/INRIAPerson/Test/pos/