A minimalistic framework for fast object detection (with a pre-trained face detector)
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gen opencv3, MIT license Nov 16, 2016
rnt compatibility with OpenCV 3 Apr 10, 2018
LICENSE moved to the MIT License Jun 30, 2016
README.md minor edits to README.md Oct 21, 2015


Pixel Intensity Comparison-based Object detection (pico)

Those of you who would like to quickly see what this repository is all about, go to the folder rnt/sample. There you will find a sample program which will detect faces in a video stream supplied from the default webcam attached to the computer. Also, you can check out a demo video at http://www.youtube.com/watch?v=1lXfm-PZz0Q.

In general, detection can be described as a task of finding the positions and scales of all objects in an image that belong to a given appearance class. For example, these objects could be cars, pedestrians or human faces. Automatic object detection has a broad range of applications. Some include biometrics, driver assistance, visual surveillance and smart human-machine interfaces. These applications create a strong motivation for the development of fast and accurate object detection methods.

The pico framework is a modifcation of the standard Viola-Jones object detection method. The basic idea is to scan the image with a cascade of binary classifers at all reasonable positions and scales. An image region is classifed as an object of interest if it successfully passes all the members of the cascade. Each binary classifier consists of an ensemble of decision trees with pixel intensity comparisons as binary tests in their internal nodes. This enables the detector to process image regions at very high speed. The details are given in http://arxiv.org/abs/1305.4537.

Some highlights of pico are:

  • High processing speed.
  • There is no need for image preprocessing prior to detection.
  • There is no need for the computation of integral images, image pyramid, HOG pyramid or any other similar data structure.
  • All binary tests in internal nodes of the trees are based on the same feature type (not the case in the V-J framework).
  • The method can easily be modified for fast detection of in-plane rotated objects.

Detecting objects in images and videos

The folder rnt/ contains all the needed resources to perform object detection in images and video streams with pre-trained classification cascades. Specifically, a sample application that performs face detection can be found in the folder rnt/samples/.

Note that the library also enables the detection of rotated objects without the need of image resampling or classification cascade retraining. This is achieved by rotating the binary tests in internal tree nodes, as described in the paper.

Embedding the runtime within your application

To use the runtime in your own application, you have to:

  • Include a prototype of find_objects(...) in your code (for example, by adding #include picornt.h)
  • Compile picornt.c with your code
  • Load/include a classification cascade generated with picolrn (e.g., rnt/cascades/facefinder)
  • Invoke find_objects(...) with appropriate parameters

Notice that there are no specific library dependencies, i.e., the code can be compiled out-of-the-box with a standard C compiler.

To get a feel for how the library works, we recommend that you look at sample.c as is was specifically written to be used as documentation.

Learning custom object detectors

The program picolrn.c (available in the folder gen/) enables you to learn your own (custom) object detectors. The training data has to be provided in a specific format. The details are printed to the standard output when picolrn is invoked without parameters. It is often convenient to pipe this information into a text file:

$ ./picolrn > howto.txt

A tutorial that guides you through the process of learning a face detector can be found in the folder gen/sample/.


If you use the provided code/binaries for your work, please cite the following paper:

N. Markus, M. Frljak, I. S. Pandzic, J. Ahlberg and R. Forchheimer, "Object Detection with Pixel Intensity Comparisons Organized in Decision Trees", http://arxiv.org/abs/1305.4537


For any additional information contact me at nenad.markus@fer.hr.

Copyright (c) 2013, Nenad Markus. All rights reserved.