Titlebook: Video Analytics for Audience Measurement; First International Cosimo Distante,Sebastiano Battiato,Andrea Cavalla Conference proceedings 20

Spinal-Fusion

Face Re-Identification for Digital Signage Applications Specifically, we describe a framework employing frontal face detection technology and re-identification mechanism, based on similarity between sets of faces learned within a time slot (i.e., the models to be re-identified) and the set of face patches collected when a user appears in front a screen.

jagged

Modelling In-Store Consumer Behaviour Using Machine Learning and Digital Signage Audience Measuremenof different machine learning methods shows that by using support vector machines we can predict with 88.6 % classification accuracy whether a customer will actually make a purchase, which outperforms classification accuracy of a baseline (majority) classifier by 7.5 %. A similar approach can also b

Tailor

Pervasive Retail Strategy Using a Low-Cost Free Gaze Estimation Systemibility to automatically understand the behavior of the persons looking at a shop window: this is done by a gaze estimation technique that uses a RGB-D device in order to extract head pose information from which a fast geometric technique then evaluates the focus of attention of the persons in the s

arousal

inculpate

Immobilize

Features Descriptors for Demographic Estimation: A Comparative Study audience statistics in digital signage as well as the human interaction in social robotic environment needs of increasingly robust algorithm for gender, race and age classification. In the present paper some of the state of the art features descriptors and sub space reduction approaches for gender,

刺耳

Comparison of Facial Alignment Techniques: With Test Results on Gender Classification Taskchniques that are widely accepted in literature and measures their effect on gender classification task. There is no special reason on selecting gender classification task, any other task could have been chosen. In audience measurement systems, many important demographics, i.e. gender, age, facial e

Gene408

散步

Online Audience Measurement System Based on Machine Learning Techniquestection, face tracking, gender recognition, age classification and statistics analysis. AdaBoost classifier is utilized for face detection. A modification of Lucas and Kanade algorithm is introduced on the stage of tracking. Novel gender and age classifiers based on adaptive features, local binary p

Detain

Modelling In-Store Consumer Behaviour Using Machine Learning and Digital Signage Audience Measuremen temporal features. The collected audience measurement data can be used as a unique basis for statistical analysis of viewing patterns, interactive display applications and also for further research and observer modelling. Here, we use machine learning methods on real-world digital signage viewershi