Recognition of Repetitive Sequential Human Activity

We present a novel framework for recognizing repetitive sequential events performed by human actors with strong temporal dependencies and potential parallel overlap. Our solution incorporates sub-event (or primitive) detectors and a spatiotemporal model for sequential event changes. We develop an effective and efficient method to integrate primitives into a set of sequential events where strong temporal constraints are imposed on the ordering of the primitives. In particular, the combination process is approached as an optimization problem. A specialized Viterbi algorithm is designed to learn and infer the target sequential events and handle the event overlap simultaneously. To demonstrate the effectiveness of the proposed framework, we report detailed quantitative analysis on a large set of cashier checkout activities in a retail store.

• Type: Conference
• Year: 2009
• Where: CVPR
• Authors: Akira Yanagawa, Arun Hampapur, Quanfu Fan, Russell Bobbitt, Sharath Pankanti, Yun Zhai
• Keywords: Computer Vision, CVPR 2009, Human Activity Recognition