******************************************************************** CALL FOR PAPERS IEEE Transactions on Circuits and Systems for Video Technology Special Issue on Video Analysis on Resource-Limited Systems ******************************************************************** Schedule ----------------- Submission deadline: **Dec. 15, 2010** Notification of acceptance: Jun. 15, 2011 Final manuscript due: Jun. 30, 2011 Tentative publication date: Oct. 2011 Guest Editors ----------------- - Rama Chellappa, University of Maryland, USA - Andrea Cavallaro, Queen Mary, University of London, UK - Ying Wu, Northwestern University, USA - Caifeng Shan, Philips Research, The Netherlands - Yun (Raymond) Fu, University at Buffalo (SUNY), USA - Kari Pulli, Nokia Research Center (NRC) Palo Alto, USA In many real-world video analytics systems, the available resources are limited. This could mean low-quality data (e.g., limited imaging resolution/sensor size/frame rate), such as video footage from surveillance cameras and videos captured by consumers via mobile or wearable cameras. Another dimension comes from limited amount of processing power, for example, on mobile camera phones. There is a huge demand for video analysis and computer vision techniques on resource-limited systems. However, video analysis of low-quality video data or on devices with limited computing power is still an under-explored field. The existing video analysis research mainly focuses on high-performance systems, that is, high-quality video data or powerful computing platforms. There are many challenges when addressing video analysis on resource-limited systems. For example, How to effectively extract representative visual features from low-quality data? How to fuse multiple low-resolution frames for reliable recognition? How to accelerate vision algorithms for use on mobile platforms? How to mitigate degrading factors caused by the low-quality data? We have to adapt the existing approaches developed for high-performance systems or find new techniques suitable for resource-limited systems. This special issue seeks to present and highlight the latest developments on video analysis and computer vision on resource-limited systems. Submissions that address real-world applications are especially encouraged. Topics of interest include, but are not limited to, Feature extraction from low-quality data Super-resolution Video stabilization Object detection in low-quality data Visual tracking on resource-limited systems Image recognition on mobile devices Face image analysis on resource-limited systems (Soft-)biometrics (face, body, gait, ) in low-quality data Gesture recognition in low-quality data Human activity analysis in low-quality data Video analysis on resource-limited platforms (UAVs, toy robots, capsule endoscopy, ) Energy optimization for video coding on resource-limited devices Multiple-view analysis of low-quality data Video editing on mobile devices SLAM (simultaneous localization and mapping) on low-power devices Low-cost smart camera networks with embedded computing Video analysis on low-cost non-classical cameras (e.g., omni-directional cameras) Real-world applications on resource-limited systems (smart environments, safety and surveillance, entertainment ) Evaluation of video analysis algorithms on resource-limited systems