Conversational Group Detection With Deep Convolutional Networks

Detection of interacting and conversational groups from images has applications in video surveillance and social robotics. In this paper we build on prior attempts to find conversational groups by detection of social gathering spaces called o-spaces used to assign people to groups. As our contributions to the task, we are the first paper to incorporate features extracted from the room layout image, and the first to incorporate a deep network to generate an image representation of the proposed o-spaces. Specifically, this novel network builds on the PointNet architecture which allows unordered inputs of variable sizes. We present accuracies which demonstrate the ability to rival and sometimes outperform the best models, but due to a data imbalance issue we do not yet outperform existing models in our test results.

Does Your Phone Know Your Touch?

This paper explores supervised techniques for continuous anomaly detection from biometric touch screen data. A capacitive sensor array used to mimic a touch screen as used to collect touch and swipe gestures from participants. The gestures are recorded over fixed segments of time, with position and force measured for each gesture. Support Vector Machine, Logistic Regression, and Gaussian mixture models were tested to learn individual touch patterns. Test results showed true negative and true positive scores of over 95% accuracy for all gesture types, with logistic regression models far outperforming the other methods. A more expansive and varied data collection over longer periods of time is needed to determine pragmatic usage of these results.