39,708 research outputs found
Markerless Motion Capture in the Crowd
This work uses crowdsourcing to obtain motion capture data from video
recordings. The data is obtained by information workers who click repeatedly to
indicate body configurations in the frames of a video, resulting in a model of
2D structure over time. We discuss techniques to optimize the tracking task and
strategies for maximizing accuracy and efficiency. We show visualizations of a
variety of motions captured with our pipeline then apply reconstruction
techniques to derive 3D structure.Comment: Presented at Collective Intelligence conference, 2012
(arXiv:1204.2991
Tracking Table Tennis Balls in Real Match Scenes for Umpiring Applications
Judging the legitimacy of table tennis services presents many challenges where technology can be judiciously applied to enhance decision-making. This paper presents a purpose-built system to automatically detect and track the ball during table-tennis services to enable precise judgment over their legitimacy in real-time. The system comprises a suite of algorithms which adaptively exploit spatial and temporal information from real match video sequences, which are generally characterised by high object motion, allied with object blurring and occlusion. Experimental results on a diverse set of table-tennis test sequences corroborate the system performance in facilitating consistently accurate and efficient decision-making over the validity of a service
Multi-feature Bottom-up Processing and Top-down Selection for an Object-based Visual Attention Model
Artificial vision systems can not process all the information that they receive from the world in real time because it is highly expensive and inefficient in terms of computational cost. However, inspired by biological perception systems, it is possible to develop an artificial attention model able to select only the relevant part of the scene, as human vision does. This paper presents an attention model which draws attention over perceptual units of visual information, called proto-objects, and which uses a linear combination of multiple low-level features (such as colour, symmetry or shape) in order to calculate the saliency of each of them. But not only bottom-up processing is addressed, the proposed model also deals with the top-down component of attention. It is shown how a high-level task can modulate the global saliency computation, modifying the weights involved in the basic features linear combination.Ministerio de EconomÃa y Competitividad (MINECO), proyectos: TIN2008-06196 y TIN2012-38079-C03-03. Campus de Excelencia Internacional AndalucÃa Tech
An Immersive Telepresence System using RGB-D Sensors and Head Mounted Display
We present a tele-immersive system that enables people to interact with each
other in a virtual world using body gestures in addition to verbal
communication. Beyond the obvious applications, including general online
conversations and gaming, we hypothesize that our proposed system would be
particularly beneficial to education by offering rich visual contents and
interactivity. One distinct feature is the integration of egocentric pose
recognition that allows participants to use their gestures to demonstrate and
manipulate virtual objects simultaneously. This functionality enables the
instructor to ef- fectively and efficiently explain and illustrate complex
concepts or sophisticated problems in an intuitive manner. The highly
interactive and flexible environment can capture and sustain more student
attention than the traditional classroom setting and, thus, delivers a
compelling experience to the students. Our main focus here is to investigate
possible solutions for the system design and implementation and devise
strategies for fast, efficient computation suitable for visual data processing
and network transmission. We describe the technique and experiments in details
and provide quantitative performance results, demonstrating our system can be
run comfortably and reliably for different application scenarios. Our
preliminary results are promising and demonstrate the potential for more
compelling directions in cyberlearning.Comment: IEEE International Symposium on Multimedia 201
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