Multimodal 3-D Tracking and Event Detection via the Particle Filter

Determining the occurrence of an event is fundamental to developing systems that can observe and react to them. Often, this determination is based on collecting video and/or audio data and determining the state or location of a tracked object. We use Bayesian inference and the particle filter for tracking moving objects, using both video data obtained from multiple cameras and audio data obtained using arrays of microphones. The algorithms developed are applied to determining events arising in two fields of application. In the first, the behavior of a flying echolocating bat as it approaches a moving prey is studied, and the events of search, approach and capture are detected. In a second application we describe detection of turn-taking in a conversation between possibly moving participants recorded using a smart video conferencing setup.

• Type: Conference
• Year: 2001
• Where: EVENT
• Authors: Dmitry N. Zotkin, Ramani Duraiswami, Larry S. Davis
• Keywords: Audio Data, Bayesian Inference, EVENT 2001, Video Data