|title:||Video content analysis & aggression detection system for a train environment|
|published in:||November 2007|
Master of Science thesis
Man-machine interaction group
Delft University of Technology
|PDF (8640 KB)|
Aggression in trains is increasing annually, the aggression is costing the Dutch railroads NS a lot of money due to destroying, or drawing graffiti on the interior of the train. On the other hand, the aggression towards the train conductors is causing conductors to quit their job, which is not what the NS wants due to the shortage they have in conductors. Passengers are also not feeling safe on the train. Current video-surveillance systems have limited intelligence, necessitating the employment of human operators who are able to interpret the images. This job is tedious and has a low efficiency: Only a small fraction of the image stream contains interesting information.
At Delft University of Technology, there is an ongoing project on aggression detection in trains by multiple single modality devices that capture data from different modalities, hence "multimodal". Using video and sound input, an intelligent system, has to be developed that is able to make a context sensitive interpretation of human behavior on a train. The aim is to detect aggression as it is about to happen.
In this thesis we investigate the behavior of the human in the train. We designed and implemented a system with high usability standards with which users can annotate situations in the train compartments. We are particularly interested in aggression. The input to the annotating process is images that are captured from video data of recorded scenarios of aggressive and non aggressive situation. We implemented a user interface that is connected to a rule-based expert system to handle the incoming data from the annotating process. The output gives an aggression level and an aggression classification.
We designed and implemented a demonstration and have tested the system. The model, implementation and test results will be described in this thesis.