This work has been performed in industrial context and presents people detection and tracking. High performances, system autonomy and ease of deployment, and the real-time processing are the most important constraints which have guided this work. Some parts of the proposed work are already integrated and deployed in a commercial product while others are in prototype state and are planned to be integrated in future. People detection aims to localise and delimits people in video sequences and static images. The proposed people detection is performed using a cascade of classifiers trained using LogitBoost algorithm on region covariance descriptors. A state of the art approach, providing good performances but not applicable for real time is taken as basis and is optimized and improved to process in real time while the detection performances are increased. Our optimization scheme is generalizable to many other kinds of detectors based on cascade of classifiers where the whole space of all possible weak classifiers cannot be reasonably tested. People tracking in mono-camera context aims to provide a set of reliable images of every observed person by each camera, to extract his visual signature for further re-identification purpose. It provides also some real world information which is useful to improve re-identification process. It is achieved by tracking SIFT features using a specific particle filter, in addition to a data association framework based on global optimization, which infers object tracking from SIFT points one, and which deals with most of possible cases, especially occlusions.