Integral Channel Features (ICF), also known as ChnFtrs, is a method for object detection in computer vision.
This method was highly exploited by Dollár et al. in their work for pedestrian detection, that was first described at the BMVC in 2009.
The performance was evaluated in terms of pedestrian detection rates at the reference point of 10 - 4 fppw (false positive per window).
The ICF method (ChnFtrs) has been widely exploited by researchers in Computer Vision after the work was initially published by Dollar et al..
Several authors have obtained even better performance by either extending feature pool in various ways or by carefully choosing the classifier and training it with a larger dataset.
Work by Zhang et al also exploited integral channel features in developing Informed Haar detector for pedestrian detection.
[4] They used the same combination of channels as Dollár et al. but were able achieve approximately 20% higher performance than the baseline ChnFtrs method.
Further, Benenson et al. were able to increase the detection speed of baseline ChnFtrs method by avoiding the need to resize input image.
The performance of a base detector developed by Dollár et al. has been shown to be enhanced by adding better prior knowledge and training with a larger dataset.