A Scalable Approach to Activity Recognition based on Object Use

We propose an approach to activity recognition based on detecting and analyzing the sequence of objects that are being manipulated by the user. In domains such as cooking, where many activities involve similar actions, object-use information can be a valuable cue. In order for this approach to scale to many activities and objects, however, it is necessary to minimize the amount of human-labeled data that is required for modeling. We describe a method for automatically acquiring object models from video without any explicit human supervision. Our approach leverages sparse and noisy readings from RFID tagged objects, along with common-sense knowledge about which objects are likely to be used during a given activity, to bootstrap the learning process. We present a dynamic Bayesian network model which combines RFID and video data to jointly infer the most likely activity and object labels. We demonstrate that our approach can achieve activity recognition rates of more than 80% on a real-w...

• Type: Conference
• Year: 2007
• Where: ICCV
• Authors: Jianxin Wu, Adebola Osuntogun, Tanzeem Choudhury, Matthai Philipose, James M. Rehg
• Keywords: Activity Recognition, Activity Recognition Rates, Computer Vision, ICCV 2007, Object Models, RFID Tagged Objects, Visual Object Recognition