Smart Meeting Rooms
Summary of Project
Computer vision-based monitoring is used
for automated recognition of the activities of participants in a meeting. A
head tracker, originally developed for monitoring in a home environment, was
evaluated for this smart meeting application using the PETS-ICVS 2003 video
data sets. The shape of each person's head was modeled as approximately
elliptical whilst internal appearance was modeled using color histograms.
Gradient and color cues were combined to provide measurements for tracking
using particle filters. A particle filter based on Iterated Likelihood
Weighting (ILW) was used which, in conjunction with the broad likelihood
responses obtained, achieved accurate tracking even when the motion model was
poor. It was compared to the widely used Sampling Importance Re-sampling (SIR)
algorithm. Results are reported for tracking and recognition of the actions of
the six meeting participants in the PETS-ICVS data. ILW outperformed standard
SIR and reliably tracked all participants throughout the meeting scenarios.
Objectives
- To develop computer vision systems that can monitor a group people interacting in a meeting room.
- To track the participants in a meeting automatically, and recognise their actions (entering the room, sitting, standing, going to the white board.
Data
Set Description
The smart meeting
room environment consists of two cameras: one mounted on each of two opposing
walls as shown in the following images:
Camera-1 Camera 2
The sequence began
with each of the six participants in turn entering then sitting down.
Subsequently, each in turn stood up, walked to the whiteboard, wrote something
and then returned to his seat twice. Finally, each person in turn exited the
room. Each person is tracked throughout entire sequence and the actions of
entering, exiting, sitting down, getting up, and going to the whiteboard are
recognized.
Results
Head
Tracking:
Camera1 (avi file) Camera2 (avi file)
Camera1 (avi file) Camera2 (avi file)
Action Recognition:
Camera-1 Camera 2