# Aggregate channel features ACF Pedestrian Detector

> Aggregate Channel Features for pedestrian detection: the ten LUV and gradient channels, how they are extracted with cv::Sobel, and the first results.

- Canonical URL: https://www.funvisiontutorials.com/blog/aggregate-channel-features-acf-pedestrian-detector/
- Author: Vladimir Kucera
- Published: 2015-12-09
- Updated: 2023-11-02
- Topics: Computer vision, Object detection, Image processing, Docker & deployment
- Videos: https://www.youtube.com/watch?v=YKcJT8aBpeQ

I am testing Aggregated Channel Features ACF \[1\] for mode than 6 months.  Features are extracted from 10 channels, L LUV, U LUV, V LUV, 0° Grad, 30° Grad, 60° Grad, 90°Grad, 120° Grad, 150° Grad, and Mag Gradient.
This 10 channels are relatively simple to extract mainly by sobel derivatives (cv::sobel).
Yes, the implementation is mainly based on Opencv.

I am using this model param int modelRows = 88;
                                        int modelCols =modelRows/4;

Init features are randomly generated. This is funny part. In some cases program hold 5GB is Ram memory and can cause shutdown of my PC. I cant find why. :)

Now, I am trying different approach of weak feature selectors by AdaBoost, GentleBoost, WaldBoost .

This is only personal research.
In our applications.
Waldboost on Haar + LBP  features + Kalman are fast and good enough for me.

I know that Opencv Contrib module has ICF ACF feature extractor and Detector learned by waldboost. After some testing i try to implement my own version and this is the first results.

### Adaboost ACF learning details

-   I am using Adaboost 1000 weak classifier,
-   Ped dataset is my own. 10 000 positive samples. I am working on this dataset time to time for more than 2 years.
-   8 000 Neg samples are generated randomly from my Travel pictures :)

Yes first results after 6 months.

## Aggregate channel features first results

Video: https://www.youtube.com/watch?v=YKcJT8aBpeQ

In this example is used Town Center Dataset only for demonstration. I can not find any condition of usage on this page. [http://www.robots.ox.ac.uk Town Centre Dataset](http://www.robots.ox.ac.uk/ActiveVision/Research/Projects/2009bbenfold_headpose/project.html#datasets)

\[1\] P.Dollar, R.Appel, S.Belongie and P.Perona. "Fast feature pyramids for object detection". TPAMI, 2014. 1, 2, 7