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Human Gesture Analysis
Collaborative Gesture Analysis in Multi-Camera Networks
An architecture for opportunistic discovery of gesture elements
for analysis of human gestures in a multi-camera sensor
network is developed in this project. The proposed approach
is motivated by the diversity of gestures expressed
in passive monitoring applications, and is based on the concept
of opportunistic fusion of simple features within a single
camera and active collaboration between multiple cameras
in the decision making process.
This means that within a single camera, a number of simple
features are aggregated adaptively for the model, whereas
between multi-view cameras, collaboration is pursued in different
levels to employ the available pieces of information to
provide a description of the gesture.
By reducing the uncertainty
through different levels of collaboration, the proposed opportunistic
approach offers the potential to address gesture
recognition problems more efficiently and accurately.





Real-Time Pose Analysis for Gaming
a network of smart cameras is used to develop a real-time gaming application
based on detecting the user’s posture. We employ a wireless smart camera with
a programmable Single-Instruction Multiple-Data (SIMD)
processor dedicated for image processing. Another embedded
processor also sits inside the smart camera which takes
in the output of the SIMD processor for further processing
and then sends the results through a ZigBee channel. As
the outputs of the processors in the smart cameras, the centroids
of the body parts are detected and sent to the central PC to observe the bandwidth
constraints of ZigBee. In the central PC, noise is filtered
both within single camera’s data and also by combining observations
from two cameras. This helps to mitigate detection jitter caused by sensor
noise in the cameras. Finally the 3D skeleton model of
the user is estimated from the 2D body part centroids. As the application,
we place two users into a virtual ball-play game to create
interactions between the users and the system. The system
operates in real time with a 30 frames/sec rate.



Collaborative Face Orientation and Profile Analysis
Most face recognition and tracking techniques employed
in surveillance and human-computer interaction (HCI) systems
rely on the assumption of a frontal view of the human
face. In alternative approaches, knowledge of the orientation
angle of the face in captured images can improve the
performance of techniques based on non-frontal face views.
In this work, we developed a collaborative technique for face
analysis in smart camera networks with a dual objective of
detecting the camera view closest to a frontal view of the
subject, and estimating the face orientation angles in all the
camera views based on additional fusion of local angle estimates.
Soft information indicating the probabilities of face
and eye candidates in each image is exchanged between the
cameras, and epipolar geometry mapping is employed to assess
correspondence between candidates in different views.
Once the camera with the closest view to the frontal face
view is identified, further exchange of the face orientation
angles estimated by all cameras allows for a collaborative
refinement of the estimates according to their associated confidence
levels. The develpoped collaborative detection and
estimation schemes employ low-complexity algorithms and
do not require image transfer between the cameras. Hence,
these schemes are applicable to networks of image sensors
with in-node processing and narrowband wireless communication.


Publications
Toward Low Latency Gesture Control using Smart Camera Network (Link coming soon)
Z. Zivkovic, V. Kliger, A. Danilin, B. Schueler, C. Chang, R. Kleihorst, and H. Aghajan,
CVPR 2008 Workshop on Embedded Computer Vision (ECVW), June 2008.
Real-Time Human Posture Reconstruction in Wireless Smart Camera Networks (Link coming soon)
C. Wu, R. Kleihorst, and H. Aghajan,
Information Processing in Sensor Networks (IPSN-SPOTS), April 2008.
Pose and Gaze Estimation in Multi-Camera Networks for Non-Restrictive HCI
C. Chang, C. Wu, and H. Aghajan,
Int. Conf. on Computer Vision -- Workshop on HCI, Oct. 2007.
From Distributed Vision Networks to Human Behavior Interpretation
H. Aghajan and C. Wu,
Behaviour Monitoring and Interpretation Workshop at the 30th German Conference on Artificial Intelligence, Sept. 2007.
Linear Dynamic Data Fusion Techniques for Face Orientation Estimation in Smart Camera Networks
C. Chang and H. Aghajan,
1st Int. Conf. on Distributed Smart Cameras (ICDSC), Sept. 2007.
Model-based Human Posture Estimation for Gesture Analysis in an Opportunistic Fusion Smart Camera Network
C. Wu and H. Aghajan,
Int. Conf. on Advanced Video and Signal based Surveillance (AVSS), Sept. 2007.
A LQR Spatiotemporal Fusion Technique for Face Profile Collection in Smart
Camera Surveillance
C. Chang and H. Aghajan,
Int. Conf. on Advanced Video and Signal based Surveillance (AVSS), Sept. 2007.
A Multi-Touch Surface Using Multiple Cameras (Link coming soon)
I Katz, K. Gabayan, and H. Aghajan,
Advanced Concepts for Intelligent Vision Systems (ACIVS), August 2007.
Spatiotemporal Fusion Framework for Multi-Camera Face Orientation Analysis
C. Chang and H. Aghajan,
Advanced Concepts for Intelligent Vision Systems (ACIVS), August 2007.
Model-based Image Segmentation for Multi-View Human Gesture Analysis
C. Wu and H. Aghajan,
Advanced Concepts for Intelligent Vision Systems (ACIVS), August 2007.
Layered and Collaborative Gesture Analysis in Multi-Camera Networks
H. Aghajan and C. Wu,
Int. Conf. on Acoustics, Speech, and Signal Processing (ICASSP), April 2007.
Opportunistic Feature Fusion-based Segmentation for Human Gesture Analysis in Vision Networks
C. Wu and H. Aghajan,
IEEE SPS-DARTS, March 2007.
Collaborative Gesture Analysis in Multi-Camera Networks
C. Wu and H. Aghajan,
ACM SenSys Workshop on Distributed Smart Cameras (DSC), Oct. 2006.
Collaborative Face Orientation Detection in Wireless Image Sensor Networks
C. Chang and H. Aghajan,
ACM SenSys Workshop on Distributed Smart Cameras (DSC), Oct. 2006.