|title:||Body Posture Analysis in the Context of Shopping|
|author:||Alper Kemal Koç|
|published in:||June 2011|
Master of Science thesis
Man-machine interaction group
Delft University of Technology
|PDF (4.643 KB)|
AbstractShopping is a daily common activity for all individuals. In that context, there are needs related to security, efficiency and satisfaction. Store owners, customers and producer brands are three parties sharing these needs and there are intelligent systems that offer solutions. Intelligent systems could use the video information from the store and they can offer basic information about what the people in the store are doing by interpreting body language.
In this research to address those questions we aim to design a system which can automatically detect the basic actions in a store that are performed by people. We define the basic actions that are most commonly observed in a store and by considering sequences of these actions, higher level information about customers? behavior can be extracted.
We set up a shopping environment for experiments and made recordings in which people are doing shopping and performing the defined basic actions. We analyze the obtained data to examine the common properties and patterns of shopping related actions.
Next step is to extract the discriminative features from those recordings that can reveal the actions they belong to. For that part we use two tools, ETH Human Pose Detection framework and Kinect sensor developed by Microsoft. ETH Tool detects and returns the limbs of a person in the scene and we use this information to extract the angles between the limbs automatically. Kinect is capable of returning the depth information, people?s silhouettes and if configured properly also the body skeleton coordinates. Furthermore the information obtained from silhouettes and body skeleton coordinates are used to extract different types of features. Next we evaluate the two tools and the sets of features with different classifiers by employing the developed automatic action detection software module.
To conclude we examine the shopping store data, evaluate ETH and Kinect tools with different sets of features and yield to conclusions about those actions and the problem itself. The action detection performance is not very high yet that leads us to a lot of interpretations and deeper knowledge about those actions and possible solutions for addressing the challenges of the analyzed problem.