Evaluation of Methods for Person Re-identification between Non-overlapping Surveillance Cameras

Sammanfattning: This thesis describes a comparison of several state-of-the-art methods used for re-identification of a person between several non-overlapping views captured by surveillance cameras. Since 2014, the focus of the area of person re-identification has been heavily oriented towards approaches employing the use of neural network due to the increase in performance shown from this approach. Three different methods employing convolutional neural networks as a means of attempting automatic person re-identification have mainly been evaluated in this thesis. These three methods are named Spatial-Temporal Person Re-identification (ST-reID), Top DropBlock Network (Top-DB-Net), and Adaptive L2 Regularization. A fourth method known as Multiple Expert Brainstorming Network (MEB-Net) using domain adaptation is used for comparison to the results of applying the trained models from the other three methods on an unseen environment. As an attempt at improving the results of applying the models on an unseen environment, two different approaches have been taken. The first of these is an attempt at segmenting the person from the background by creating a mask that encapsulates the person while disregarding the background, as opposed to using a rectangular cropped image for training and evaluating the methods. To do this, Mask-RCNN which is a framework for object instance segmentation is used. The second approach explored in this thesis is attempting automatic white balancing as a means of removing the effect of the illumination source of the scenes before the person images are extracted. Both approaches show positive results when the model is applied on an unseen environment as opposed to using the unchanged person images, although the results have not been able to match those that have been obtained using domain adaptation.