Pfinder: Real-Time Tracking of the Human Body
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Trainable System for Object Detection
International Journal of Computer Vision - special issue on learning and vision at the center for biological and computational learning, Massachusetts Institute of Technology
Model-Based Estimation of 3D Human Motion
IEEE Transactions on Pattern Analysis and Machine Intelligence
Example-Based Object Detection in Images by Components
IEEE Transactions on Pattern Analysis and Machine Intelligence
Detecting Faces in Images: A Survey
IEEE Transactions on Pattern Analysis and Machine Intelligence
Trial by Fire: Teleoperated Robot Targets Chernobyl
IEEE Computer Graphics and Applications
Urban Search and Rescue Robots: From Tragedy to Technology
IEEE Intelligent Systems
Potential Tasks and Research Issues for Mobile Robots in RoboCup Rescue
RoboCup 2000: Robot Soccer World Cup IV
Pedestrian Detection Using Wavelet Templates
CVPR '97 Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition (CVPR '97)
Vision-Based Traffic Surveillance System on the Internet
ICCIMA '99 Proceedings of the 3rd International Conference on Computational Intelligence and Multimedia Applications
Face recognition: A literature survey
ACM Computing Surveys (CSUR)
Learning to track 3D human motion from silhouettes
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Deformable templates for face recognition
Journal of Cognitive Neuroscience
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There are two significant challenges to standard approaches to detect humans through computer vision. First, scenarios when the poses and postures of the humans are completely unpredictable. Second, situations when there are many occlusions, i.e., only parts of the body are visible. Here a novel approach to perception is presented where a complete 3D scene model is learned on the fly to represent a 2D snapshot. In doing so, an evolutionary algorithm generates pieces of 3D code that are rendered and the resulting images are compared to the current camera picture via an image similarity function. Based on the feedback of this fitness function, a crude but very fast online evolution generates an approximate 3D model of the environment where non-human objects are represented by boxes. The key point is that 3D models of humans are available as code sniplets to the EA, which can use them to represent human shapes or portions of them if they are in the image. Results from experiments with real world data from a search and rescue application using a thermal camera are presented.