Enhancing person re-identification: leveraging DensePose for improving occlusion handling and generalization

Sammanfattning: In this master’s thesis we propose a DensePose-based person re-identification (re-ID) machine learning algorithm building upon previous research on this topic. DensePose, a deep neural network that performs human body part segmentation on images, forms the foundation of our approach. We investigate whether utilization of DensePose can enhance performance on re-ID algorithms with the utilization of several different loss functions. Furthermore, we examine if the segmentation can be of benefit when dealing with occluded data samples. Our model uses DensePose as regularization through exploitation of the densely semantically aligned body part images (DSAP-images) the segmentation network provides. We adapt terminology from previous work and use two deep convolutional neural network streams, a main full image stream (MF-stream) which processes original images of the dataset, and a densely semantically aligned guiding stream (DSAG-stream) which processes the DSAP-images. The DSAG-stream is utilized as a regularizing stream which helps training the MF-stream in learning relevant local features in the full images. In the inference, the DSAG-stream is discarded, allowing the MF-stream to independently evaluate on the test data. All model training and testing is conducted on the Market-1501 dataset and our best performing model (which uses a linear combination of triplet loss, ID loss and center loss) obtains a CMC-Rank 1 score of 91.4 % and a mAP score of 78.1 %. Our DensePose-based model is able to increase performance on re-ID in comparison to similar non-DensePose-based models. It does however perform worse on occluded samples but demonstrates significant potential in terms of generalization abilities when applied to unfamiliar data.