Overview

To detect multiple objects of interest, the methods based on Hough transform use non-maxima supression or mode seeking in order to locate and to distinguish peaks in Hough images. Such postprocessing requires tuning of extra parameters and is often fragile, especially when objects of interest tend to be closely located. In the paper, we develop a new probabilistic framework that is in many ways related to Hough transform, sharing its simplicity and wide applicability. At the same time, the framework bypasses the problem of multiple peaks identification in Hough images, and permits detection of multiple objects without invoking nonmaximum suppression heuristics. As a result, the experiments demonstrate a significant improvement in detection accuracy both for the classical task of straight line detection and for a more modern category-level (pedestrian) detection problem.

Images/Videos

Iterations of the Hough-like algorithm for multiple object instances detection:

Databases

We used image datasets TUD-crossing and TUD-campus containing mostly profile views of pedestrians in relatively crowded locations. The original annotations provided include only the pedestrians occluded by no more than 50%. As we were interested in the performance of the method under significant overlap, we reannotated the data by marking all pedestrians whose head and at least one leg were clearly visible.

Download annotatons.

Sourñe Code

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Neither Microsoft nor any contributor to the software will be liable for any damages related to the software or this msr-ssla, including direct, indirect, special, consequential or incidental damages, to the maximum extent the law permits, no matter what legal theory it is based on. Also, you must pass this limitation of liability on whenever you distribute the software or derivative works.

When using this software, please acknowledge the effort that went into development by referencing the corresponding paper. Note that this is not the original software that was used for the paper mentioned below. It is a re-implementation and requires the Open Computer Vision Library.

If you have questions concerning the source code, please contact obarinova@graphics.cs.msu.ru.

Download c++ code for pedestrians detection

• original Visual Studio 2005 solution  or
• Linux Port by Dr. Rodrigo Benenson.

Download c++ code for lines detection

• the latest version, which is much faster and  more accurate (Visual Studio 2008 solution)
• the older version (Visual Studio 2005 solution).

Publications

Olga Barinova, Victor Lempitsky, Pushmeet Kohli, On detection of Multiple object instances using Hough transform, CVPR 2010 [pdf]

Slides from CVPR 2010 [zip]

Talk at CVPR 2010 [link]

Acknowledgements

This project is supported by Microsoft Research programs in Russia. V. Lempitsky is also supported by EU under ERC grant VisRec no. 228180.