Riemannian Manifold-Based Support Vector Machine for Human Activity Classification in Images

This paper addresses the issue of classification of human activities in still images. We propose a novel method where part-based features focusing on human and object interaction are utilized for activity representation, and classification is designed on manifolds by exploiting underlying Riemannian geometry. The main contributions of the paper include: (a) represent human activity by appearance features from image patches containing hands, and by structural features formed from the distances between the torso and patch centers; (b) formulate SVM kernel function based on the geodesics on Riemannian manifolds under the log-Euclidean metric; (c) apply multi-class SVM classifier on the manifold under the one-against-all strategy. Experiments were conducted on a dataset containing 2750 images in 7 classes of activities from 10 subjects. Results have shown good performance (average classification rate of 95.83%, false positive 0.71%, false negative 4.24%). Comparisons with three other related classifiers provide further support to the proposed method

Simulation-based visual analysis of individual and group dynamic behavior

The article presents a new framework for individual and group dynamic behavior analysis with wide applicability to video surveillance and security, accidents and safety management, customer insight and computer games. It combines graphical multi-agent simulation and motion pattern recognition for performing visual data analysis using an object-centric approach. The article describes the simulation model used for modeling the individual and group dynamics which is based on the analytical description of dynamic trajectories in closed micro-worlds and the individual and group behavior patterns exhibited by the agents in the visual scene. The simulator is implemented using 3D graphics tools and supports real-time event log analysis for pattern recognition and classification of the individual and group agent’s behavior

Person Re-identification by Video Ranking

Abstract. Current person re-identification (re-id) methods typically rely on single-frame imagery features, and ignore space-time information from image sequences. Single-frame (single-shot) visual appearance matching is inherently limited for person re-id in public spaces due to visual ambiguity arising from non-overlapping camera views where viewpoint and lighting changes can cause significant appearance variation. In this work, we present a novel model to automatically select the most discriminative video fragments from noisy image sequences of people where more reliable space-time features can be extracted, whilst simultaneously to learn a video ranking function for person re-id. Also, we introduce a new image sequence re-id dataset (iLIDS-VID) based on the i-LIDS MCT benchmark data. Using the iLIDS-VID and PRID 2011 sequence re-id datasets, we extensively conducted comparative evaluations to demonstrate the advantages of the proposed model over contemporary gait recognition, holistic image sequence matching and state-of-the-art singleshot/multi-shot based re-id methods.

Workshop on Emerging Surveillance Capabilities & Requirements

On 5-6 July 2011, the Joint Research Center (JRC) of the European Commission organized a workshop on “Emerging Surveillance Capabilities and Requirements”. Around 40 R&D managers, researchers and practitioners from key European RTD institutes or academia, operators and leading system / equipment providers attended the workshop. The workshop was hosted by the Institute for the Protection and Security of the Citizen (IPSC) at JRC (http://ipsc.jrc.ec.europa.eu/), in Ispra, Italy. The purpose of the workshop was to provide a forum where key scientists and practitioners could meet to address the following issues: (a) Review the technology state of the art and the operational requirements; (b) Anticipate technology trends and future needs, including testing and standards; (c) Consider legal, regulatory and ethical aspects. Some key-elements which arose during these sessions are worth being highlighted here: The overview of current European projects and national initiatives showed some common concerns in terms of issues being addressed and including the following main topics standardization, testing and performance assessment under increasingly complex scenarios (365/24/7 video analytics service level, multiple image/video sources and sensors), impact of societal requirements/issues, such as privacy and security, in the planning, design and implementation phase of surveillance systems. European networking and multi-disciplinary cooperation were often mentioned as relevant means to improve surveillance capabilities as well as to significantly enhance awareness of the societal values of concern. More precisely, the following points were cited: - Surveillance capabilities need to be federated and coordinated at the European level. - A mutual interest for cooperation for surveillance has been identified with the main aims of sharing best practices and common standards; the exchange of scientific staff as well as the use of national capabilities by other countries are some of the potential means to achieve it. The organization of workshops on the regular yearly basis was also suggested. - Trend to pervasive observation / monitoring rendering data protection and privacy even more important issues which it is suggested to address in a future workshop. Some new challenges for surveillance to be addressed in a very short-term future were introduced by several participants. A European certification framework for smart surveillance, the development of common privacy and performance testing methodologies, need for reference datasets, are some of these challenges. This report summarizes the workshop’s contents and the main findings agreed during the closing session. It also contains all the PowerPoint format documents presented except the ones for which a confidentiality clause was required.JRC.G.7-Digital Citizen Securit

Interaction Analysis in Smart Work Environments through Fuzzy Temporal Logic

Interaction analysis is defined as the generation of situation descriptions from machine perception. World models created through machine perception are used by a reasoning engine based on fuzzy metric temporal logic and situation graph trees, with optional parameter learning and clustering as preprocessing, to deduce knowledge about the observed scene. The system is evaluated in a case study on automatic behavior report generation for staff training purposes in crisis response control rooms

Interaction Analysis in Smart Work Environments through Fuzzy Temporal Logic

Interaction analysis is defined as the generation of situation descriptions from machine perception. World models created through machine perception are used by a reasoning engine based on fuzzy metric temporal logic and situation graph trees, with optional parameter learning and clustering as preprocessing, to deduce knowledge about the observed scene. The system is evaluated in a case study on automatic behavior report generation for staff training purposes in crisis response control rooms