[UPDATE] Glow 2017: looking for student assistants for large crowd management experiment

TU/e and Philips Lighting Research are preparing an unprecedented crowd management experiment to be conducted at the Glow light festival in November 2017 in Eindhoven. The Crowdflow Research Group, TU/e, has developed a unique pedestrian tracking system based on overhead Kinect sensors and custom processing software. This set-up will be deployed at Glow to record and analyse a flow of several 100.000’s of people. We look for student assistants to help set-up the system for Glow and get it operational. If you are pragmatic, proficient in python, web and linux programming, and if you can work under pressure of a big festival, please contact Alessandro Corbetta (

Minimum required commitment is one day per week.

Call QR code:



Presenting @ AVSS 17, Tuesday 29th of August

On Tuesday 29th of August I’ll be in Lecce at the “14-th IEEE International Conference on Advanced Video and Signal-Based Surveillance” presenting our work 

  • Weakly supervised training of deep convolutional neural networks for overhead pedestrian localization in depth fields
  • A. Corbetta, V. Menkovski, F. Toschi
  • Link

The presentation will be in at the “Signal Processing for Understanding Crowd Dynamics” workshop. 

Come by if you are around!


MSc final projects available!

We are looking for enthusiastic Msc students in Physics or Applied Mathematics for final projects in statistical crowd dynamics.

The projects, developed within the group of Prof. F. Toschi (WDY), involve an exciting mixture of fundamental physics research and technological development. They are addressed to IT & computing enthusiasts willing to face state-of-the-art research challenges.

For further information please contact us via email.

Dense crowd dynamics analysis: from agglomerative clustering to deep learning

We employ clustering algorithms to isolate individual pedestrians in Kinect 3D depth maps. High crowd densities or unusual body shapes increase the error rate of such approach. Deep learning based image recognition techniques showed promising results in the analysis of dense crowds. A systematic development of such techniques in our specific context for real-time and offline data analysis is a natural next step. The main objectives of this project are:

 building datasets from our extensive recordings at Eindhoven station and/or at Naturalis museum for systematic benchmarking;
 formulating heuristics to improve performance of current agglomerative clustering approaches;
 employing deep learning image recognition techniques such as Faster-rCNN to reduce the error rate and classify non-usual detections (children, bikes,…).

Keywords: dense crowd dynamics | agglomerative clustering | GPU/CUDA-based deep learning | convolutional nets | parallel computing.

Far-range crowd dynamics

Microsoft Kinect sensors are quite limited in depth range (~6 m) and produce depth maps in VGA resolution. Recent depth cameras such as Zed, by Stereolabs, allow FullHD depthmaps with range up to 20m, even in sunlight. These specifications enable more flexible outdoor crowd tracking setups with less sensors, possibly not strictly overhead. The main objectives of this project are:

 integrate Zed sensors into our current real-time crowd tracking environment;
 device algorithms for non-overhead crowd dynamics analysis;
 pursue real-time crowd analyses in outdoor crowded scenes.

Successful Zed sensors setups will upgrade our crowd tracking system in Stratumseind.
Keywords: outdoor crowd analysis | Zed stereo cameras | GPU-based depth maps processing | projective geometry | parallel computing.

Crowd flux analytics

Robust estimates of pedestrian fluxes are paramount in any pedestrian facility to assess, e.g., current occupancy. Reliable automatic estimates are a challenge at high densities or in presence e.g. of kids school groups. We expect our real time pedestrian tracking to improve state-of-the art flux assessments with both scientific and technological aims. The main objectives of this project are:

 device algorithms for robust real time crowd flux estimation from Kinect-based noisy tracking;
 analyze and model real life pedestrian arrival stochastic processes;
 device heuristics to detect irregular/rare/dangerous events from tracking.

This project involves fast paced testing and deployment in our measurement locations, among others, Naturalis museum, Leiden.

Keywords: arrival processes | flux assessment from noisy tracking | real time data analysis.

Dense and diluted statistical crowd dynamics on a wide corridor

In the period September 2014-April 2015 we performed 24/7 crowd tracking at Eindhoven train station aiming at an unprecedented crowd data collection for statistical analysis. Such measurements are enabling data-driven particle-based models at all density levels.
The main objectives of this project are:

 map the dataset for homogenous traffic conditions employing both typical observables and pattern matching;
 improve current particle-based models for pedestrian dynamics to reproduce (statistically) the observed dynamics;
 device heuristics to detect irregular/rare/dangerous events from tracking.

Keywords: statistical mechanics | mathematical modeling | large scale data analysis| parallel computing.