Latency insensitive task scheduling for real-time video processing and streaming

In recent times, computer vision and pattern recognition (CVPR) technologies made automatic feature extraction, events detection possible in real-lime, on-the-fly video processing and streaming systems. However, these multiple and computational expensive video processing tasks require specialized processors to ensure higher frame rate output. We propose a framework for achieving high video frame rate using a single processor high-end PC while multiple, computational video tasks such as background subtraction, object tracking, recognition and facial localization have been performed simultaneously. We show the framework in detail, illustrating our unique scheduler using latency insensitive tasks distribution and the execution coulent parameters generation function (PGF). The experiments have indicated successful results using high-end consumer type PC. © Springer-Verlag Berlin Heidelberg 2005.