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We propose a calibration method that automatically estimates the extrinsic calibration between a sensor pose-graph from natural scenes. The sensor pose-graph represents a system of sensors comprising of lidars and cameras, without sensor co-visibility constraints. The method addresses the fact that each scene contributes differently to the calibration problem by introducing a diligent scene selection...
We propose an automatic, targetless, data-driven, extrinsic calibration method to calibrate push-broom 2D lidars with a multi-camera system. The calibration problem is decoupled into alternating optimisers over two hierarchical levels, where both levels are linked with a penalty term. The lower-level optimises the six degrees-of-freedom (DoF) rigid-body transforms between the lidar and each camera...
We propose a control mechanism to obtain opportunistic high resolution facial imagery, via distributed constrained optimization of the pan, tilt, zoom (PTZ) parameters for each camera in a sensor network. The objective function of the optimization problem quantifies the per camera per target image quality. The tracking constraints, which are a lower bound on the information about the estimated position...
The performance of video-based scene analysis algorithms often suffers because of the inability to effectively acquire features on the targets. In this paper, we propose a distributed approach for dynamically controlling the pan, tilt, zoom (PTZ) parameters of a PTZ camera network so as to maximize system performance, through opportunistic acquisition of high quality images. The cameras gain utility...
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