In this project I have designed a model of features, human behavior and intentions. The model suggests a set of features that can be used to describe the interaction between a human being and an automated sliding door. The model also defines symbols representing value sets for the features. The symbols are then combined in order to describe different events, mapping features to intentions. This model provides a framework guiding the capturing process as well as the reasoning process.Further, I have designed a mechanism for capturing human movement and extracting the features as suggested by the model of features, human behavior and intentions. The solution components are based on research done within computer vision, where different tools and algorithms were reviewed and evaluated. Parts of the suggested solution are provided as software libraries, while others had to be implemented. The solution includes using an Xbox Kinect as a sensor device, and the OpenNI framework together with the middleware NITE for Human body tracking and skeletal joint extraction.A reasoning mechanism was designed, that utilizes the designed model in order to reach a conclusion about the intention of a human interacting with the door. Different reasoning techniques were reviewed in context of the sliding doors problem. Based on the review I suggest using rule-based reasoning. By using the events described in the model and by giving values to the different symbols I was able to form the rules for the reasoning process.The designed mechanisms were put together in an implementation in C/C++ comprising depth and RGB image capture, body tracking, user handling and feature extraction, rule-based reasoning and door control.A motorized sliding door was built, together with a door controller allowing a computer to interface with the door, giving open and close commands.Finally, the door was tested both through a live demo and a laboratory style, structured observation. The door proved a superior performance to the traditional sliding doors when it came to identifying negative intentions, thus reducing the number of false positives drastically. However, both false positives and false negatives occurred, leaving room for improved accuracy.With my solution I have managed to interpret the intention of a user interacting with an automated sliding door. I have lifted the reasoning process to a symbolic level, dealing with symbols and events easy to understand. Although the model is limited to a very specific domain, and the solution has got some limitations and weaknesses, this is a good starting point for further work.