PERSON RE-IDENTIFICATION & VIDEO-BASED HEART RATE ESTIMATION
In order to distinguish essays and pre-prints from academic theses, we have a separate category. These are often much longer text based documents than a paper.
Estimation of physiological vital signs such as the Heart Rate (HR) has attracted a lot of attention due to the increase interest in health monitoring. The most common HR estimation methods such as Photoplethysmography(PPG) require the physical contact with the subject and limit the movement of the subject. Video-based HR estimation, known as videoplethysmography (VHR), uses image/video processing techniques to estimate remotely the human HR. Even though various VHR methods have been proposed over the past 5 years, there are still challenging problems such as diverse skin tone and motion artifacts. In this thesis we present a VHR method using temporal difference filtering and small variation amplification based on the assumption that HR is the small color variations of skin, i.e. micro blushing. This method is evaluated and compared with the two previous VHR methods. Additionally, we propose the use of spatial pruning for an alternative of skin detection and homomorphic filtering for the motion artifact compensation.
Intelligent video surveillance system is a crucial tool for public safety. One of the goals is to extract meaningful information efficiently from the large volume of surveillance videos. Person re-identification (ReID) is a fundamental task associated with intelligent video surveillance system. For example, ReID can be used to identity the person of interest to help law enforcement when they re-appear in the different cameras at different time. ReID can be formally defined as establishing the correspondence between images of a person taken from different cameras. Even though ReID has been intensively studied over the past years, it is still an active research area due to various challenges such as illumination variations, occlusions, view point changes and the lack of data. In this thesis we propose a weighted two stream train- ing objective function which combines the Siamese cost of the spatial and temporal streams with the objective of predicting a person’s identity. Additionally, we present a camera-aware image-to-image translation method using similarity preserving Star- GAN (SP-StarGAN) as the data augmentation for ReID. We evaluate our proposed methods on the publicly available datasets and demonstrate the efficacy of our methods.