Behavioral analysis in an urban environment is a complex task that requires material and human resources, due to the difficulty of interpreting the situations. This paper presents a method to improve the detection of dangerous behaviors by assisting surveillance stations. Our objective is to alert when one of these behaviors is captured by a surveillance camera. To do this, we analyze the positions and paths of the persons in a global way, through a group of parameters. These parameters are determined by an automatic image analysis algorithm such as DBSCAN computed on an NVIDIA Jetson TX2. This analysis allows to detect, through the evolution and clustering of points in each cloud, phenomena qualified as abnormal, such as dispersion and rapid clustering, as well as poaching. The data used to feed our algorithm come from simulations that allow testing new and different scenarios. The performance of our proposed method is evaluated on videos representing real case situations.
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