# Object and Event Detection Pipeline for Rink Hockey Games
## 2 Computer Vision Applied to Hockey Sports
In the same work, the jersey color of each of the detected players was extracted in order to determine their team affiliations. A filter was incorporated into the proposed model to exclude distracting information, such as the audience and sideline advertising bars, thereby refining the detection of the targeted players. This resulted in accurate detection with a precision of 98.75% and a recall of 94.11% for individual players and an average accuracy of 93.05% for team classification using a dataset of collected images from the 2018 Winter Olympics. The authors argued that the custom-built dataset enabled their player detection model  
Figure 1: Ice hockey vs. rink hockey (images: Wikimedia Commons). to achieve the best results when compared to some state-of-the-art approaches such as YOLOv3 [13], Faster R-CNN [14], and CornerNet [15]. Another challenge in risk hockey object detection is the need for real-time performance given the fast-paced nature of the sport and the requirement for object detection updates to be made with regularity. Researchers have proposed a number of solutions to this problem, including the use of lightweight CNN architectures [16] and the implementation of the object detection pipeline on specialized hardware like graphics processing units (GPUs) [17] or field-programmable gate arrays (FPGAs) [18]. In [16], a CNN with three branches and a classification network with four cascades was presented. The cascaded networks were initially trained using labeled image patches and subsequently applied to an entire image by employing a dilation testing strategy. The authors claimed that their approach achieved state-of-the-art accuracy on three types of games (basketball, soccer, and ice hockey) with 1000 fewer parameters than those of CNNs that were adapted from general object detection networks such as Faster R-CNN. The work presented in [17] proposed an approach to locate the puck, which is the key component of an ice hockey game. One of the principal challenges is the puck's small size. The motion blur caused by the puck's rapid movement, the occlusion between the puck and other objects, and the visual noise (such as the advertisements on the rink) presented additional difficulties for the project. The author proposed a two-stage model for the detection of minute objects. The initial stage involved a two-dimensional CNN that summarized the representation of each frame. In the second stage, the fusion of the per-frame representations was fed into a three-dimensional CNN in order to decode the video's temporal information. The proposed approach achieved a precision of 90.8% and a recall of 86.7%. The F1 score reached 88.7%, which is higher than the performance of YOLOv3 [13] (F1 score = 0.685) and Mask RCNN [19] (F1 score = 0.749). In [18], a hardware accelerator was presented that implemented a YOLO CNN for real-time object detection with high throughput and power efficiency. The parameters of the implemented YOLO were retrained and also quantized using binary weight and flexible low-bit activation. The binary weight technique enabled the storage of the entire network model in block RAMs of a field-programmable gate array (FPGA). This approach resulted in a reduction in off-chip accesses, thereby achieving a performance boost. All convolutional layers have been pipelined in order to optimize hardware utilization. Additionally, the input images were delivered and processed in a sequential manner to the hardware accelerator. Similarly, the output of the previous layer was transmitted in a sequential manner to the subsequent layer. The intermediate data were fully reused across layers, thereby eliminating memory accesses from the outside. A reduction in DRAM accesses resulted in a reduction in power consumption. Furthermore, the authors argued that the fully parameterized convolutional layers facilitated straightforward scaling of the network.