Art gallery theorems and algorithms
Art gallery theorems and algorithms
Automatic Sensor Placement from Vision Task Requirements
IEEE Transactions on Pattern Analysis and Machine Intelligence - Special Issue on Industrial Machine Vision and Computer Vision Technology:8MPart
Face Recognition: Features Versus Templates
IEEE Transactions on Pattern Analysis and Machine Intelligence
Learning and Recognizing Human Dynamics in Video Sequences
CVPR '97 Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition (CVPR '97)
Design of many-camera tracking systems for scalability and efficient resource allocation
Design of many-camera tracking systems for scalability and efficient resource allocation
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
A General Method for Sensor Planning in Multi-Sensor Systems: Extension to Random Occlusion
International Journal of Computer Vision
Estimating pedestrian counts in groups
Computer Vision and Image Understanding
Multi-Camera Human Activity Monitoring
Journal of Intelligent and Robotic Systems
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Cameras are becoming a common tool for automated vision purposes due to their low cost. Many surveillance and inspection systems include cameras as their sensor of choice. How useful these camera systems are is very dependent upon the positioning of the cameras. This is especially true if the cameras are to be used in automated systems as a beneficial camera placement will simplify image processing operations. Therefore, a reliable positioning algorithm can lower the processing requirements of the system. In this paper several considerations for improving camera placement are investigated with the goal of developing a general algorithm that can be applied to a variety of systems. This paper presents this algorithm for placement problem in the context of computer vision and robotics. Simulated results of our method are then shown and discussed, along with an outline of future work.