Detection Of People Stream Features Using Eigenvalue Maps


  • Wilbert van de Ridder University of Twente
  • Claudia Ruffoni Politecnico di Milano
  • Daan Geijs
  • Geert Pingen



Large crowd video surveillance is important with respect to safety. Especially in the case of unexpected events it is beneficial to be able to detect certain features like bottlenecks as quick as possible. A number of methods have been proposed to find such occurrences but accuracy is still lacking. Our research expands on a previously presented method in order to improve the detection rate of important features. This project focusses only on bottlenecks. Eigenvalue maps derived from Jacobian matrices resulting from opical flow analysis are used to find bottlenecks in people streams. An accuracy of \textgreater 80\% was obtained using a varied but small dataset. The results indicate that using eigenvalue maps for feature detection are feasible and more reliable compared to earlier proposed similar methods.

Author Biography

Wilbert van de Ridder, University of Twente

Robotics and Mechatronics


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Advanced Computer Vision and Pattern Recognition