Jordi Gonzàlez, Josep Mª Gonfaus, Carles Fernández, F.Xavier Roca V&L Net workshop on Vision and Language Brighton, September 15 th, 2011 Exploiting Natural-Language.

Slides:



Advertisements
Similar presentations
D2 Conceration, Vilamoura, April 16th Video & Image Indexing and Retrieval in the Large Scale V T A L A S Progress Nozha Boujemaa, Scientific Coordinator.
Advertisements

2.3.3 MAD SAMBA (Multicamera and distributed Surveillance and multisensor-based surveillance) Contact: Alessandro ZANASI zanasi-alessandro.eu.
National Technical University of Athens Department of Electrical and Computer Engineering Image, Video and Multimedia Systems Laboratory
9th E3 Concertation Meeting, Brussels, September 10th, 2002
A Natural Interactive Game By Zak Wilson. Background This project was my second year group project at University and I have chosen it to present as it.
Borough of Verona 2014 Using axis technology. Two Types of Day and Night Vision Technologies Wide dynamic range (WDR) describes an attribute of an imaging.
Martin Wagner and Gudrun Klinker Augmented Reality Group Institut für Informatik Technische Universität München December 19, 2003.
The Steerable Projector and Camera Unit in an Instrumented Environment Lübomira Spassova Saarland University, Saarbrücken, Germany.
Y A S O O B A L I b o r n o n 1 9 t h F e b r u a r y i n K a n p u r d i s t r i c t o f U t t a r P r a d e s h. H e s t a r t e d s i n g i.
Visual Event Detection & Recognition Filiz Bunyak Ersoy, Ph.D. student Smart Engineering Systems Lab.
Computer Vision REU Week 2 Adam Kavanaugh. Video Canny Put canny into a loop in order to process multiple frames of a video sequence Put canny into a.
1 Texmex – November 15 th, 2005 Strategy for the future Global goal “Understand” (= structure…) TV and other MM documents Prepare these documents for applications.
Chapter 11 Beyond Bag of Words. Question Answering n Providing answers instead of ranked lists of documents n Older QA systems generated answers n Current.
Recent Developments in Human Motion Analysis
AceMedia Personal content management in a mobile environment Jonathan Teh Motorola Labs.
CS335 Principles of Multimedia Systems Multimedia and Human Computer Interfaces Hao Jiang Computer Science Department Boston College Nov. 20, 2007.
1 Introduction What IS computer vision? Where do images come from? the analysis of digital images by a computer.
WP -6: Human Tracking and Modelling Year–I Objectives: Simple upper-body models and articulated tracks from test videos. Year-I Achievements: Tracking.
H.264 Remote Surveillance & Streaming Solution Darim 2010 Darim Vision Co., Ltd.
NATIONAL TECHNICAL UNIVERSITY OF ATHENS Image, Video And Multimedia Systems Laboratory
1 ICT: what EFL Teachers need to know today. 2 General outline 4 Why use the Net for ELT? 4 What skills are needed? 4 Lesson planning/ teaching 4 Practical.
ABOUT ME Hussein Al Osman Assistant Professor, EECS Started in September 2014 Background: Undergraduate and Graduate studies at the University of Ottawa.
Multimedia Information Retrieval and Multimedia Data Mining Chengcui Zhang Assistant Professor Dept. of Computer and Information Science University of.
Summary - Part 3 - Objectives The purpose of this basic IP technology training is to explain video over IP network. This training describes how video can.
10/7/2015art.live - 3 rd IST Concertation meeting 3 rd IST Concertation Meeting art.live (IST 10942) Xavier Marichal, UCL ADERSA - ADETTI - Casterman -
Object Based Processing for Privacy Protected Surveillance Karl Martin Kostas N. Plataniotis University of Toronto Dept. of Electrical and Computer Engineering.
GRAPPLE - Public Event Eindhoven NL January 22, 2011 Slide 1 Adaptivity in Virtual Reality using the GALE Engine Prof. dr. Olga De Troyer WISE, Vrije Universiteit.
Risk Management. What we offer? We provide IP video monitoring solutions for safety and security through our systems integration capabilities.
IST Programme - Key Action III Semantic Web Technologies in IST Key Action III (Multimedia Content and Tools) Hans-Georg Stork CEC DG INFSO/D5
Augmented Reality and 3D modelling By Stafford Joemat Supervised by Mr James Connan.
Prototype 3: MI prototype for video surveillance and biometry CVDSP-UJI Computer Vision Group – UJI Digital Signal Processing Group – UV November 2010.
CAMEO: Year 1 Progress and Year 2 Goals Manuela Veloso, Takeo Kanade, Fernando de la Torre, Paul Rybski, Brett Browning, Raju Patil, Carlos Vallespi, Betsy.
Lecture 1 Mei-Chen Yeh 03/02/2010. Announcements TA: 游宗毅 Assignment #1 due 03/09.
FP7-ICT Networked Media and Search Systems End-to-end Immersive and Interactive Media Technologies “creating a pervasive Augmented Reality paradigm,
Bachelor of Engineering In Image Processing Techniques For Video Content Extraction Submitted to the faculty of Engineering North Maharashtra University,
Human Activity Recognition at Mid and Near Range Ram Nevatia University of Southern California Based on work of several collaborators: F. Lv, P. Natarajan,
WELCOME TO ALL. DIGITAL IMAGE PROCESSING Processing of images which are Digital in nature by a Digital Computer.
Soon Joo Hyun Database Systems Research and Development Lab. US-KOREA Joint Workshop on Digital Library t Introduction ICU Information and Communication.
BACKGROUND MODEL CONSTRUCTION AND MAINTENANCE IN A VIDEO SURVEILLANCE SYSTEM Computer Vision Laboratory 指導教授:張元翔 老師 研究生:許木坪.
Target Tracking In a Scene By Saurabh Mahajan Supervisor Dr. R. Srivastava B.E. Project.
K.U. K.U. & Leuven & Leuven 2 Computer Vision Labs Prof. Luc Van Gool ETH - Switzerland Un. Leuven – Belgium appr. 15 researchers Tracking Recognition.
ENTERFACE’08 Multimodal Communication with Robots and Virtual Agents mid-term presentation.
Some research areas:  Medicine: ◦ analysis of bio-signals, ◦ medical imaging ◦…◦… 1.
  Computer vision is a field that includes methods for acquiring,prcessing, analyzing, and understanding images and, in general, high-dimensional data.
Copyright 2007 by Rombix. R CyClops is a computer vision solution which could integrate most of the Real World Computer Vision Application. Available.
What is Multimedia Anyway? David Millard and Paul Lewis.
3D Motion Classification Partial Image Retrieval and Download Multimedia Project Multimedia and Network Lab, Department of Computer Science.
TRECVID IES Lab. Intelligent E-commerce Systems Lab. 1 Presented by: Thay Setha 05-Jul-2012.
REAL-TIME DETECTOR FOR UNUSUAL BEHAVIOR
Digital Video Library - Jacky Ma.
IP Speed Dome Solution 2006-Feb-22.
Technologies: for Enhancing Broadcast Programmes with Bridgets
3D Motion Classification Partial Image Retrieval and Download
Can computers match human perception?
زبان بدن Body Language.
Chapter 10 Image Segmentation.
An Introduction of Marker and Markerless In AR
Towards lifelike Computer Interfaces that learn
Plankton Classification VIDI: Sign Recognition HANDWRITING RECOGNITION
Knowledge-based event recognition from salient regions of activity
Rethink VIDEO security
3rd Studierstube Workshop TU Wien
Using Natural Language Processing to Aid Computer Vision
Face Detection Gender Recognition 1 1 (19) 1 (1)
Applications Discussion
CS 332 Visual Processing in Computer and Biological Vision Systems
Outline Kulkarni, P., Ganesan, D., Shenoy, P., and Lu, Q. SensEye: a multi-tier camera sensor network. In Proceedings of the 13th Annual ACM international.
Discussion Class 9 Informedia.
Evaluate the integral {image}
Presentation transcript:

Jordi Gonzàlez, Josep Mª Gonfaus, Carles Fernández, F.Xavier Roca V&L Net workshop on Vision and Language Brighton, September 15 th, 2011 Exploiting Natural-Language Interaction in Video Surveillance Systems

2  UNDERSTAND videos with humans … (or actions in images)  …to EXPLAIN them in their context (or classify, search…) ISE Lab: Research Lab on Image Sequence Evaluation

3 Detection Segmentation Agent/body/face tracking Activity recognition Scene understanding Behavior recognition Video annotation/ retrieval Augmented reality NL descriptions human horse ISE Lab: some research we do...

An example: the MIPRCV project 156 : … 203 : Lo vianant surt per la part inferior dreta :... 4

5 Control Terminal 3 Dedicated Servers Giga Ethernet cam Servolens with integrated Zoom Pan & Tilt Network Infrastructure An example: the MIPRCV demonstrator

6 PTZ control

7 Terminal for Active Video Surveillance

8 Natural Language Understanding

What happened today? 9

Jordi Gonzàlez, Josep Mª Gonfaus, Carles Fernández, F.Xavier Roca Exploiting Natural-Language Interaction in Video Surveillance Systems