Topology Dictionary for 3D Video Understanding

By Tony Tung and Takashi Matsuyama

This paper presents a novel approach that achieves 3D video understanding. 3D video consists of a stream of 3D models of subjects in motion. The acquisition of long sequences requires large storage space (2 GB for 1 min). Moreover, it is tedious to browse data sets and extract meaningful information. We propose the topology dictionary to encode and describe 3D video content. The model consists of a topology-based shape descriptor dictionary which can be generated from either extracted patterns or training sequences. The model relies on 1) topology description and classification using Reeb graphs, and 2) a Markov motion graph to represent topology change states. We show that the use of Reeb graphs as the high-level topology descriptor is relevant. It allows the dictionary to automatically model complex sequences, whereas other strategies would require prior knowledge on the shape and topology of the captured subjects. Our approach serves to encode 3D video sequences, and can be applied for content-based description and summarization of 3D video sequences. Furthermore, topology class labeling during a learning process enables the system to perform content-based event recognition. Experiments were carried out on various 3D videos. We showcase an application for 3D video progressive summarization using the topology dictionary.

http://www.computer.org/cms/Computer.org/transactions/tpami/pdfs/ttp2012081645.pdf

iSpy turns your PC into a full security and surveillance system

iSpy uses your cameras, webcams, IP cams and microphones to detect and record movement or sound. Captured media is compressed to flash video or mp4 and streamed securely over the web and local network. iSpy can run on multiple computers simultaneously and has full Email, SMS and MMS alerting functions and remote viewing.

http://www.ispyconnect.com/

An Adaptive Video Retrieval System Based On Recent Studies On User Intentions While Watching Videos Online

Article from http://cie.acm.org/articles/adaptive-video-retrieval-system-based-recent-studies-user-intentions-while-watching-videos-online/

By Christoph Lagger , Mathias Lux , Oge Marques

We have developed a prototype of an adaptive video retrieval system that leverages the knowledge of users’ intentions (and their relationship to video genres/categories) uncovered by our recent studies on “user intentions while watching videos online” [1] to provide better search results and a user interface adapted to the intentions and needs of its users. The goal behind the development of this prototype is to provide a better solution to the problem of including the user’s context into video retrieval than the one offered by baseline video retrieval interfaces (e.g., YouTube). The prototype is called “You(r)Intent.” 

The diagram of the prototype (see Figure 1) consists of three main blocks: (i) The user interface, which includes a text input box for typing a query and four buttons to communicate the intention to the system, namely:

• to learn something,
• to be entertained,
• to get informed, or
• to solve a task;

(ii) A ruleset, derived from the results of our studies [1], so that videos whose categories provide higher correlation with the user’s intention are ranked higher in the search results; and (iii) A collection of sources of video content, e.g., Vimeo, YouTube, or Khan Academy.

Figure 1: Block diagram of the prototype for an adaptive video retrieval system.

For better understanding, we have outlined a simple example scenario. Let us assume a user wants to learn about a specific topic and she types the query “moonwalk.” Since the user has a “learning intention” in mind, she clicks on the 'I want to learn something’ button. Our pattern-based ruleset then optimizes the search to certain sources and categories. In this example case, YouTube and Khan Academy might be used as video sources and the videos will be ranked by categories from the strongest to the weakest correlation to the user’s intention [1], leading to a result screen whose screenshot appears in Figure 2. Notice how three clips from the “How-to & Style” category (all of which are related to the moonwalk dance popularized by Michael Jackson) appear at the top of the screen, with the second-highest-ranked category (“Science & Technology”) appearing in a second block, mostly containing NASA footage from historical Apollo-era moon explorations. By prioritizing the categories that are most strongly correlated to the user’s intentions and adopting a visually pleasant layout that shows them in easily distinguishable blocks, we circumvent the ambiguity caused by the query term and provide an intuitive way to navigate to the desired result.

Figure 2: Result screen of the prototype when the user queries for videos containing the “moonwalk” keyword and expresses the intention to learn something.

After finishing the development of the first version of the prototype, we performed a user survey, asking users to perform specific video retrieval tasks, and to report on how easily they carried out those tasks and how satisfied they were with the results. Evaluation methods used for this survey included: observation of the interviewee, analysis of mouse tracking heat maps, activity logging, and semi-structured interviews with the participants. Overall, participants solved each task somewhat easily and were very satisfied while working with the prototype. Moreover, the position at which a video of interest (which would solve the task at hand) appears was considered satisfactory throughout all tests. 

Reference

[1] C. Lagger, M. Lux and O. Marques, “What Makes People Watch Online Videos: An Exploratory Study”,ACM Computers in Entertainment (2012) [submitted to]

Janken (rock-paper-scissors) Robot with 100% winning rate

The purpose of this study is to develop a janken (rock-paper-scissors) robot system with 100% winning rate as one example of human-machine cooperation systems.
http://www.k2.t.u-tokyo.ac.jp/fusion/Janken/index-e.html

Recognition of human hand can be performed at 1ms with a high-speed vision, and the position and the shape of the human hand are recognized. The wrist joint angle of the robot hand is controlled based on the position of the human hand. The vision recognizes one of rock, paper and scissors based on the shape of the human hand. After that, the robot hand plays one of rock, paper and scissors so as to beat the human being in 1ms.

This technology is one example that show a possibility of cooperation control within a few miliseconds. And this technology can be applied to motion support of human beings and cooperation work between human beings and robots etc. without time delay.

Article from congeo.gr

Predicting events in videos, before they happen. CVPR 2012 Best Paper

Article from http://quantombone.blogspot.gr/2012/06/predicting-events-in-videos-before-they.html

Intelligence is all about making inferences given observations, but somewhere in the history of Computer Vision, we (as a community) have put too much emphasis on classification tasks.  What many researchers in the field (unfortunately this includes myself) focus on is extracting semantic meaning from images, image collections, and videos.  Whether the output is a scene category label, an object identity and location, or an action category, the way we proceed is relatively straightforward:

• Extract some measurements from the image (we call them "features", and SIFTand HOG are two very popular such features)
• Feed those features into a machine learning algorithm which predicts the category these features belong to.  Some popular choices of algorithms are Neural Networks, SVMs, decision trees, boosted decision stumps, etc.
• Evaluate our features on a standard dataset (such as Caltech-256, PASCAL VOC, ImageNet, LabelMe, etc)
• Publish (or as is commonly know in academic circles: publish-or-perish)

While only in the last 5 years has action recognition become popular, it still adheres to the generic machine vision pipeline.  But let's consider a scenario where adhering to this template can hav disastrous consequences.  Let's ask ourselves the following question:

Q: Why did the robot cross the road?

A: The robot didn't cross the road -- he was obliterated by a car. This is because in order to make decisions in the world you can't just wait until all observations happened.  To build a robot that can cross the road, you need to be able to predict things before they happen! (Alternate answer: The robot died because he wasn't using Minh's early-event detection framework, the topic of today's blog post.)

This year's Best Student Paper winner at CVPR has given us a flavor of something more,something beyond the traditional action recognition pipeline, aka "early event detection."  Simply put, the goal is to detect an action before it completes.  Minh's research is rather exciting, which opens up room for a new paradigm in recognition.  If we want intelligent machines roaming the world around us (and every CMU Robotics PhD student knows that this is really what vision is all about), then recognition after an action has happened will not enable our robots to do much beyond passive observation.  Prediction (and not classification) is the killer app of computer vision because classification assumes you are given the data and prediction assumes there is an intent to act on and interpret the future.

While Minh's work focused on simpler actions such as facial recognition, gesture recognition, and human activity recognition, I believe these ideas will help make machines more intelligent and more suitable for performing actions in the real world.

Disgust detection example from CVPR 2012 paper

To give the vision hackers a few more details, this framework uses Structural SVMs (NOTE: trending topic at CVPR) and is able to estimate the probability of an action happening before it actually finishes.  This is something which we, humans, seem to do all the time but has been somehow neglected by machine vision researchers.

Max-Margin Early Event Detectors.
Hoai, Minh & De la Torre, Fernando
CVPR 2012

Abstract:
The need for early detection of temporal events from sequential data arises in a wide spectrum of applications ranging from human-robot interaction to video security. While temporal event detection has been extensively studied, early detection is a relatively unexplored problem. This paper proposes a maximum-margin framework for training temporal event detectors to recognize partial events, enabling early detection. Our method is based on Structured Output SVM, but extends it to accommodate sequential data. Experiments on datasets of varying complexity, for detecting facial expressions, hand gestures, and human activities, demonstrate the benefits of our approach. To the best of our knowledge, this is the first paper in the literature of computer vision that proposes a learning formulation for early event detection.
Early Event Detector Project Page (code available on website)
Minh gave an excellent, enthusiastic, and entertaining presentation during day 3 of CVPR 2012 and was definitely one of the highlights of that day. He received his PhD from CMU's Robotics Institute (like me, yipee!) and is currently a Postdoctoral research scholar in Andrew Zissermann's group in Oxford.  Let's all congratulate Minh for all his hard work.

Article from http://quantombone.blogspot.gr/2012/06/predicting-events-in-videos-before-they.html

Is that smile real or fake?

Do you smile when you're frustrated? Most people think they don't — but they actually do, a new study from MIT has found. What's more, it turns out that computers programmed with the latest information from this research do a better job of differentiating smiles of delight and frustration than human observers do.
The research could pave the way for computers that better assess the emotional states of their users and respond accordingly. It could also help train those who have difficulty interpreting expressions, such as people with autism, to more accurately gauge the expressions they see

Read more about this story here

Related publication:

M. E. Hoque, R. W. Picard, Acted vs. natural frustration and delight: Many people smile in natural frustration, 9th IEEE International Conference on Automatic Face and Gesture Recognition (FG'11), Santa Barbara, CA, USA, March 2011. (PDF: 1693 KB)

Article from http://www.computervisiononline.com/blog/smile-real-or-fake