CRNN Based JerseyBib NumberText Recognition in Sports and Marathon Images The primary challenge in tracing the participants in sports and marathon video or images is to detect and localize the jerseyBib number that may present in different regions of their outfit captured in cluttered environment conditions. In this work, we proposed a new framework based on detecting the human body parts such that both Jersey Bib number and text is localized reliably. To achieve this, the proposed method first detects and localize the human in a given image using Single Shot Multibox Detector (SSD). In the next step, different human body parts namely, Torso, Left Thigh, Right Thigh, that generally contain a Bib number or text region is automatically extracted. These detected individual parts are processed individually to detect the Jersey Bib numbertext using a deep CNN network based on the 2channel architecture based on the novel adaptive weighting loss function. Finally, the detected text is cropped out and fed to a CNNRNN based deep model abbreviated as CRNN for recognizing jerseyBibtext. Extensive experiments are carried out on the four different datasets including both benchmarking dataset and a new dataset. The performance of the proposed method is compared with the stateoftheart methods on all four datasets that indicates the improved performance of the proposed method on all four datasets.

Track Cyclist Detection and Identification using Mask RCNN and Kmeans Clustering Automatically identifying a cyclist as they pass the starting line in a track cycling race is primarily restricted to physical detection systems assigned to individual competitors. These current solutions are expensive and require physical hardware to be attached to the cyclists during a race. This paper proposes a method to both detect when a track cyclist passes the lap line and identify the cyclist using a region convolutional neural network (RCNN) and Kmeans clustering. This method aims to overcome several limitations of prior research by identifying multiple cyclists in a single frame, requiring only a single image to train per cyclist, and increasing identification accuracy by combining a neural network with Kmeans clustering. The proposed method uses the Mask RCNN with COCO 2017 dataset and Kmeans clustering to train the detector with a single image of each cyclist. Cyclists are then able to be identified during a race. The method achieved an average identification accuracy of 95.1% with single image training. Multiple cyclists could be detected in an individual frame through the successful integration of a combined Mask RCNN Kmeans detector. The primary limitation of the method is the reduction in identification accuracy for cyclists wearing similar coloured clothing. By using Kmeans clustering and RGB range thresholding, the cyclists may be misidentified if the colours are too similar. Further research that would benefit this solution includes developing additional pointsofinterest to identify cyclists and additional cameras with different angles for instances of total overlap between cyclists.