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.
For many high-value manufacturing applications, advanced control systems are required to ensure product quality is maintained; this requires accurate data to be collected from in-situ sensors. Making accurate in-situ measurements is challenging due to the aggressive environments found within manufacturing machines and processes. This paper investigates a method to obtain surface profile measurements in a spectral-domain, common-path, low-coherence system. A fibre based Low Coherence Interferometer was built and was used to experimentally measure surface profiles. The fringes obtained from interferograms were transformed into the Fourier domain to obtain a trackable peak relating to the surface depth. This has been illustrated with ideal step height measurements and referenced specimens as well as more challenging surface roughness measurements, which have produced complex signal processing issues. This work opens up avenues for a metrology based system where both machining and measurement system can coexist on the same plane, in aggressive environments.
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 μm to −2.59 μm and the repeatability was 0.25 μm.
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