Person re-identification (Re-ID) has become an intense research area in recent years. The main goal of this topic is to recognize and match individuals over time at the same or different locations. This task is challenging due to the variation of illumination, viewpoints, pedestrians’ appearance and partial occlusion. Previous works mainly focus on finding robust features and metric learning. Many metric learning methods convert the Re-ID problem to a matrix decomposition problem by Fisher discriminant analysis (FDA). Mahalanobis distance metric learning is a popular method to measure similarity; however, since directly extracted descriptors usually have high dimensionality, it’s intractable to learn a high-dimensional semi-positive definite (SPD) matrix. Dimensionality reduction is used to project high-dimensional descriptors to a lower-dimensional space while preserving those discriminative information. In this paper, the kernel Fisher discriminant analysis (KLFDA) [38] is used to reduce dimensionality given that kernelization method can greatly improve Re-ID performance for nonlinearity. Inspired by [47], an SPD matrix is then learned on lower-dimensional descriptors based on the limitation that the maximum intraclass distance is at least one unit smaller than the minimum interclass distance. This method is proved to have excellent performance compared with other advanced metric learning.

This thesis is concerned with the topic of person re-identification. Many tracking algorithms today cannot keep track of players reentering the scene from different angles and times. Therefore, in this thesis, current literature is explored to gather information about the topic, and a current state-of-the-art model is tested. The person re-identification techniques will be applied to Padel games due to the collaboration with PadelPlay AB. The purpose of the thesis is to keep track of players during full matches of Padel with correct identities. To this, a current state-of-the-art model is applied to an existing tracking algorithm to enhance its capabilities.  Furthermore, the purpose is broken down into two research questions. Firstly, how well does an existing person re-id model perform on Padel matches when it comes to keeping a consistent and accurate id on all players. Secondly, how can this model be improved upon to perform better in the new domain, being the sport of Padel? To be able to answer the research questions, a Padel dataset is created for benchmarking purposes. The state-of-the-art model is tested on the new dataset to see how it handles a new domain. Additionally, the same state-of-the-art model is retrained on the Padel dataset to answer the second research question.  The results show that the state-of-the-art model that is previously trained on the Market-1501 dataset is highly generalizable on the Padel dataset and performs closely to the new model that is purely trained on the Padel dataset. Although they perform alike, the new model trained on the Padel dataset is slightly better as seen through both the quantitative and qualitative evaluations. Furthermore, the application of re-identification technology to keep track of players yielded significantly higher results than conventional solutions such as YOLOv5 with Deepsort.