If for a given application, candidate tracking methods for humans need to be selected and optimized, then relevant sensor and truth data as well as appropriate assessment criteria are required. In the work reported in this contribution we used data recently collected in a riot control scenario. We then processed the sensor data using a set of tracking methods from literature. Tracking results and truth data allowed us to deduce metrics that reflect the usefulness of a tracking method for the selected scenario. The software implementation of the assessment criteria, together with sensor and truth data, forms a benchmark for tracking algorithms in a riot control scenario. It can be used by developers to optimize their tracking systems and to demonstrate their usefulness for application in a riot control scenario. The performance and robustness of optimized tracking methods can considerably improve situational awareness in a riot control scenario
Quick and precise response is essential for riot squads when coping with escalating violence in crowds. Often it is just a single person, known as the leader of the gang, who instigates other people and thus is responsible of excesses. Putting this single person out of action in most cases leads to a de-escalating situation. Fostering de-escalations is one of the main tasks of crowd and riot control. To do so, extensive situation awareness is mandatory for the squads and can be promoted by technical means such as video surveillance using sensor networks. To develop software tools for situation awareness appropriate input data with well-known quality is needed. Furthermore, the developer must be able to measure algorithm performance and ongoing improvements. Last but not least, after algorithm development has finished and marketing aspects emerge, meeting of specifications must be proved. This paper describes a multisensor benchmark which exactly serves this purpose. We first define the underlying algorithm task. Then we explain details about data acquisition and sensor setup and finally we give some insight into quality measures of multisensor data. Currently, the multisensor benchmark described in this paper is applied to the development of basic algorithms for situational awareness, e.g. tracking of individuals in a crowd
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