Plenoptic cameras can capture 3D information in one exposure without the need for structured illumination, allowing grey scale depth maps of the captured image to be created. The Lytro, a consumer grade plenoptic camera, provides a cost effective method of measuring depth of multiple objects under controlled lightning conditions. In this research, camera control variables, environmental sensitivity, image distortion characteristics, and the effective working range of two Lytro first generation cameras were evaluated. In addition, a calibration process has been created, for the Lytro cameras, to deliver three dimensional output depth maps represented in SI units (metre). The novel results show depth accuracy and repeatability of +10.0 mm to-20.0 mm, and 0.5 mm respectively. For the lateral X and Y coordinates, the accuracy was +1.56 m to −2.59 m and the repeatability was 0.25 µm.
Lytro cameras are equipped to capture 3D information in one exposure without the need for structured illumination, allowing greyscale depth maps of the captured image to be created using the Lytro desktop software. These consumer-grade lightfield cameras (Lytro) provide a cost-effective method of measuring the depth of multiple objects which is suitable for many applications. But, the greyscale depth maps generated using the Lytro cameras are in relative depth scale and hence not suitable for engineering applications where absolute depth is essential. In this research, camera control variables, environmental sensitivity, depth distortion characteristics, and the effective working range of first-and second-generation Lytro cameras were evaluated. In addition, a depth measuring technique to deliver 3D output depth maps represented in SI units (metres) is discussed in detail exhibiting the suitability of consumer-grade Lytro cameras suitability in metrological applications without significant modifications.
We describe the technique used to train and customize deep learning models to detect, track, and identify soccer players, who are recorded during soccer games using custom camera settings. The player detection model is customized to allow the detection of person class objects from video input. Two newly developed filters, spatial feature filters, and bounding box location filters have described that help in classifying players and audiences. A new tacking paradigm is illustrated to generate tracks of soccer players with fewer swaps, thereby reducing efforts of human annotators in later stages. A new method of identifying every player by detecting player t-shirt numbers has been developed and illustrated. This method provides tracks with high confidence and identity to most of the player corresponding to individual t-shirt number. Finally, we provide a unique result assessment technique to judge the performance of the complete model.
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