# A Comprehensive Review of Computer Vision in Sports:
## 6 GPU-Based Work Stations and Embedded Platforms  
To find the target, GPU-constrained devices such as Raspberry Pi, Latte Panda, Odroid Xu4 and Computer Vision were used. The disadvantages of machine learning techniques are that they provide poor or inaccurate results and have issues in predicting an unknown future data, whereas deep learning algorithms provide accurate results and also make predictions from unknown future data. Segmentation, localization and image classification are visual recognition systems which have prominent research contributions. Among embedded AI computing platforms, NVidia Jetson devices provide low-power computing and high-performance support for artificial intelligence-based visual recognition systems. Jetson modules are configured with OpenCV, cuDNN, CUDA Toolkit, L4T with LTS Linux kernel and TensorRT. The Intel Movidius Neural Compute Stick uses the Intel Movidius Neural Compute SDK in GPU-Constrained devices to deploy AI algorithms. [PERSON] et al. [203] presented a high-speed stereo vision system that can track the motion of a golf ball at a speed of 360 km/h under indoor lighting conditions. They implemented the algorithm on a field-programmable gate array board with an advanced RISE machine CPU [62] which implemented a deep learning approach to track the soccer ball on NVIDIA GTX1050 Ti GPU [43] and a deep learning algorithm on GTX 1080 ti GPU, based on CUDA 9.0 and Caffe to analyze the technical features in basketball video. Table 11 shows the basic comparison between GPU-based devices and GPU-Constraint Devices and possible deep learning algorithms to implement on various devices. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & **Jetson TX1** & **Jetson TX2** & **Jetson AGX Xavier** & \begin{tabular}{c} **Raspberry Pi** \\ **Series** \\ \end{tabular} & **Latte Panda** & **Odroid Xu4** \\ \hline \multirow{3}{*}{CPU} & Quad-core ARM & \begin{tabular}{c} Dual-core Denver \\ CPU and quad-core \\ ARM 57 \\ \end{tabular} & 64 bit CPU and \begin{tabular}{c} 63-bit quad- \\ core ARM \\ \end{tabular} & \begin{tabular}{c} Intel Cherry Train \\ quad-core CPU \\ \end{tabular} & \begin{tabular}{c} Cortex A7 \\ octa-core CPU \\ \end{tabular} \\ \hline \multirow{3}{*}{GPU} & NVIDIA Maxwell & NVIDIA Pascall & \begin{tabular}{c} Tensor cores + \\ 512-core Volta GPU \\ \end{tabular} & - & - & - \\  & with CUDA cores & 8 GB Memory & 16 GB Memory & 1 GB Memory & 4 GB Memory & \begin{tabular}{c} Stacked memory \\ of 2 GB \\ \end{tabular} \\ \hline \multirow{3}{*}{Storage} & \begin{tabular}{c} 16 GB Flash \\ Storage \\ \end{tabular} & 32 GB storage & 32 GB storage & \begin{tabular}{c} Support Micro \\ SD card \\ \end{tabular} & 64 GB storage & \begin{tabular}{c} Support Micro SD \\ Card \\ \end{tabular} \\ \hline \begin{tabular}{c} Possible DL \\ Algorithms to \\ Implement \\ \end{tabular} & \
\begin{tabular}{c} YOLO v2 and v3, tiny YOLO v3, SSD, \\ Faster–RCNN and Tracking algorithm \\ like YOLO v3 + Deep SORT, YOLO v4, YOLOR \\ \end{tabular} &
\begin{tabular}{c} YOLO, YOLO v2 and SSD-MobileNet etc. \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table} 
Table 11: Comparison between Jetson Modules and GPU-Constrained Devices. Figure 13: Instances from the APIDIS dataset [280]. A Field Programmable Gate Array (FPGA) has also been used in sports involving 3D motion capturing, object movement analysis and image recognition, etc. Table 12 describes how different researchers performed various studies of sports on hardware platforms such as FPGA and GPU-based devices and their results in terms of performance measures are listed.