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High-fidelity pedestrian tracking in real-life conditions has been animportant tool in fundamental crowd dynamics research allowing to quantifystatistics of relevant observables including walking velocities, mutualdistances and body orientations. As this technology advances, it is becomingincreasingly useful also in society. In fact, continued urbanization isoverwhelming existing pedestrian infrastructures such as transportation hubsand stations, generating an urgent need for real-time highly-accurate usagedata, aiming both at flow monitoring and dynamics understanding. Tosuccessfully employ pedestrian tracking techniques in research and technology,it is crucial to validate and benchmark them for accuracy. This is not onlynecessary to guarantee data quality, but also to identify systematic errors.  In this contribution, we present and discuss a benchmark suite, towards anopen standard in the community, for privacy-respectful pedestrian trackingtechniques. The suite is technology-independent and is applicable to academicand commercial pedestrian tracking systems, operating both in lab environmentsand real-life conditions. The benchmark suite consists of 5 tests addressingspecific aspects of pedestrian tracking quality, including accurate crowd fluxestimation, density estimation, position detection and trajectory accuracy. Theoutput of the tests are quality factors expressed as single numbers. We providethe benchmark results for two tracking systems, both operating in real-life,one commercial, and the other based on overhead depth-maps developed at TUEindhoven. We discuss the results on the basis of the quality factors andreport on the typical sensor and algorithmic performance. This enables us tohighlight the current state-of-the-art, its limitations and provideinstallation recommendations, with specific attention to multi-sensor setupsand data stitching.

Benchmarking high-fidelity pedestrian tracking systems for research, real-time monitoring and crowd control