Investigation of Siamese CNN for Person Detection & Re-Identification
Lokesh Janghel, Krishna Mohan C · 2017
Recent advances in visual tracking methods allow following a given object or individual in presence of signi fi cant clutter or partial occlusions in a single or a set of overlapping camera views. This thesis a whole framework for people detection and tracking in a camera network. The three main processing steps are addressed: people detection, Feature extraction, and people re-identi fi cation in multi-camera context. People re-identi fi cation is performed using an appearance based approach. A state of the art approach, which performs in real time, but provides various performances depending on the input data is improved to provide better performances while keeping the real-time processing advantage. The improvements are introduced at a new layer in deep learning method with the original approach of our cited journals, by replacing some of initial steps or by adding new ones.