Crime Monitor: Monitoring Criminals from Trajectory Data

The movement of criminals is an important factor used in detecting crimes. Individuals sentenced to house arrest who wears an ankle monitor have their trajectories collected periodically. Each offender using an ankle monitor must adhere to a set of rules, for instance, be at his/her home during the night. Unfortunately, some of them break such rules, also some end up committing crimes again. In this demonstration
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, we present a prototype system called Crime Monitor to monitor offenders in a semi-open regime. Crime Monitor reports the illegal activities to the police department in real-time based on trajectory features. Thus, the police can effectively prevent crimes from happening and handle them efficiently when they occur. We tackled the trajectory classification problem and used a deep learning model combining embedding with a recurrent neural network to classify illegal activities and learn the pattern regardless of who the criminal user is. We conduct experiments on a real dataset, and we show that DeepeST outperforms other approaches from state- of-the-art.