The art of computer programming, volume 3: (2nd ed.) sorting and searching
The art of computer programming, volume 3: (2nd ed.) sorting and searching
HUMO '00 Proceedings of the Workshop on Human Motion (HUMO'00)
Counting People in Crowds with a Real-Time Network of Simple Image Sensors
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Tracking People by Learning Their Appearance
IEEE Transactions on Pattern Analysis and Machine Intelligence
Contour-Based Object Detection in Range Images
3DPVT '06 Proceedings of the Third International Symposium on 3D Data Processing, Visualization, and Transmission (3DPVT'06)
A Marker-Free Watershed Approach for 2D-GE Protein Spot Segmentation
ISITC '07 Proceedings of the 2007 International Symposium on Information Technology Convergence
Segmentation and Tracking of Multiple Humans in Crowded Environments
IEEE Transactions on Pattern Analysis and Machine Intelligence
An Efficient Hillclimbing-based Watershed Algorithm and its Prototype Hardware Architecture
Journal of Signal Processing Systems
Tracking Multiple Occluding People by Localizing on Multiple Scene Planes
IEEE Transactions on Pattern Analysis and Machine Intelligence
Robust ellipsoidal model fitting of human heads
RobVis'08 Proceedings of the 2nd international conference on Robot vision
A method for counting moving people in video surveillance videos
EURASIP Journal on Advances in Signal Processing - Special issue on video analysis for human behavior understanding
A neural-based crowd estimation by hybrid global learning algorithm
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
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We present a fast and accurate method for human head detection in range images captured by a stereo camera that is positioned vertically, pointing from the roof to the ground. We show how a static grid of measure points (pylons) can outperform hill climbing techniques and how a fast median filter can be used for effective preprocessing of the range data. The Pylon Grid algorithm detects all local minima in the range image and has a linear time complexity with respect to the number of pylons. One important prerequisite for applying the Pylon Grid algorithm to human head detection is a one-to-one relationship between human heads in the scene and local minima in the range image. This is achieved in a preprocessing phase, where an orthographic projection, convexization and noise filtering is applied to the range data. The preprocessing steps also run in linear time and can be parametrized for a further trade-off between computational cost and accuracy. The method was tested with crowded scenes, where multiple dense groups of up to six people move in random directions and have physical contact.