Person Re-Identification in the wild : Evaluation and application for soccer games using Deep Learning

Sammanfattning: Person Re-Identification (ReID) is the process of associating images of the same person taken from different angles, cameras and at different times. The task is very challenging as a slight change in the appearance of the person can cause troubles in identifying them. In this thesis, the Re-Identification task is applied in the context of soccer games. In soccer games, the players of the same team wear the same outfit and colors, thus the task of Re-Identification is very hard. To address this problem, a state-of-the-art deep neural network based model named AlignedReID and a variation of it called Vanilla model are explored and compared to a baseline approach based on Euclidean distance in the image space. The AlignedReID model uses two feature extractor branches, one global and one local feature extractor. The Vanilla approach is a variation of the AlignedReID which uses only the global feature extractor branch of the AlignedReID. They are trained using two different loss functions, the Batch Hard and its soft-margin variation. The triplet loss is used, where for each loss calculation a triplet of images is used, an anchor, a positive pair (coming from the same person) and a negative pair. By comparing the metrics used for their evaluation, that is rank-1, rank-5, mean Average Precision (mAP) and the Area Under Curve (AUC), and by statistically comparing their mAPs which is assumed to be the most important metric, the AlignedReID model using the Batch Hard loss function outperforms the rest of the models with a mAP of 81\% and rank-1 \& rank-5 above 98\%. Also, a qualitative evaluation of the best model is presented using Grad-CAM, in order to figure how the model decides which images are similar by investigating in which parts of the images it focuses on to produce their embedding representations. It is observed that the model focuses on some discriminative features, such as face, legs and hands other than clothing color and outfit. The empirical results suggest that the AlignedReid is usable in real world applications, however further research to get a better understanding of the generalization to different cameras, leagues and other factors that may affect appearance would be interesting.