Discrete Relative States to Learn and Recognize Goals-based Behaviours of Group
Résumé
In a crisis management context, situation awareness is challeng- ing due to the complexity of the environment and the limited re- sources available to the security forces. The different emerging threats are difficult to identify and the behavior of the crowd (sep- arated in groups) is difficult to interpret and manage. In order to solve this problem, the authors propose a method to detect threat and understand the situation by analyzing the collective behavior of groups inside the crowd and detecting their goals . This is done ac- cording to a set of learned, goal-based, group behavior models and observation sequences of the group. The proposed method com- putes the group estimated state before using Hidden Markov Model to recognize the goal by the group behavior . A realistic emergency scenario is simulated to demonstrate the performance of the algo- rithms, where a suicide-bomber wearing a concealed bomb enters a busy urban street. The proposed algorithms achieve the detection of the dangerous person in the crowd, in order to raise an alert and also predict casualties by identifying which groups did not notice the threat. Complex Event Processing is used to compare and eval- uate the results. The algorithms were found more precise and more tolerant to noisy observations.
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