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In order to control riots in crowds, it is helpful to get ringleaders under control and pull them out of the crowd if one has become an offender. A great support to achieve these tasks is the capability of observing the crowd and ringleaders automatically by using cameras. It also allows a better conservation of evidence in riot control. A ringleader who has become an offender should be tracked across and recognized by several cameras, regardless of whether overlapping camera’s fields of view exist or not. We propose a context-based approach for handover of persons between different camera fields of view. This approach can be applied for overlapping as well as for non-overlapping fields of view, so that a fast and accurate identification of individual persons in camera networks is feasible. Within the scope of this paper, the approach is applied to a handover of persons between single images without having any temporal information. It is particularly developed for semiautomatic video editing and a handover of persons between cameras in order to improve conservation of evidence. The approach has been developed on a dataset collected during a Crowd and Riot Control (CRC) training of the German armed forces. It consists of three different levels of escalation. First, the crowd started with a peaceful demonstration. Later, there were violent protests, and third, the riot escalated and offenders bumped into the chain of guards. One result of the work is a reliable context-based method for person re-identification between single images of different camera fields of view in crowd and riot scenarios. Furthermore, a qualitative assessment shows that the use of contextual information can support this task additionally. It can decrease the needed time for handover and the number of confusions which supports the conservation of evidence in crowd and riot scenarios.
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