Title: Social Interaction Analysis Using A Multi Sensor Approach
Time: 9:30, June 16, 2016
Venue: room 202, Office Building
Humans are by nature social animals, and the interaction between humans is an integral feature of human societies. Social interactions play an important role in our daily lives: people organize themselves in groups to share views, opinions, as well as thoughts. However, as the availability of large-scale digitized information on social phenomena becomes prevalent, it is beyond the scope of practicality to analyze the big data computational assistance. Also, recent developments in sensor technology, such as the emergence of new sensors, advanced processing techniques, and improved processing hardware, provide an opportunity to improve the techniques for analyzing interactions by making use of more sensors in terms of both modality and quantity.
In this talk, the speaker will discuss the social interactions analysis from the social signal perspective in the multi-sensor setting. First, an extended F-formation system for robust interaction and interactant detection in a generic ambient sensor environment will be introduced. The results on interaction center detection and interactant detection show improvement compared to the rule-based interaction detection method. Then, the speaker will introduce her work investigating “presentation”, a special type of social interactions within a social group for presenting a topic. In this work, a new multi-sensor analytics framework is proposed with conventional ambient sensors (e.g., web camera, Kinect depth sensor, etc.) and the emerging wearable sensor (e.g., 谷歌 Glass, GoPro, etc.) for a substantially improved sensing of social interaction. Single and multi-modal analysis of each sensor type are conducted, followed by sensor-level fusion for improved presentation self-quantification. Feedback from the presenters shows a great potential for the use of such analytics. In the end, the speaker will discuss the limitations and broad vision for social interaction analysis in multi-sensor environment.
GAN Tian is currently a Scientist from Institute for Infocomm Research, A*STAR, Singapore. She received her Ph.D. degree in Computer Science from the School of Computing, National University of Singapore in 2015, B.S degree from East China Normal University in 2010. Her research interests cover social computing, video analytics, and wearable computing. She has published severl papers in top conference such as ACM Multimedia (ACMMM). Also, she has served as reviewers for top conference and journals like Transaction on Multimedia (TMM), ACM Conference on Human Factors in Computing Systems (CHI), The IEEE International Conference on Multimedia & Expo (ICME), etc.