Low Resource, Post-processed Lecture Recording from 4K Video Streams

Many universities are using lecture recording technology to expand the reach of their teaching programs, and to continue instruction when face to face lectures are not possi- ble. Increasingly, high-resolution 4K cameras are used, since they allow for easy reading of board/screen context. Unfortunately, while 4K cameras are now quite affordable, the back-end computing infrastructure to process and distribute a multitude of recorded 4K streams can be costly. Furthermore, the bandwidth requirements for a 4K stream are exorbitant - running to over 2GB for a 45-60 minute lecture. These factors mitigate against the use of such technology in a low-resource environment, and motivated our investigation into methods to reduce resource requirements for both the institution and students. We describe the design and implementation of a low resource 4K lecture recording solution, which addresses these problems through a computationally efficient video processing pipeline. The pipeline consists of a front-end, which segments presenter motion and writing/board surfaces from the stream and a back-end, which serves as a virtual cinematographer (VC), combining this contextual information to draw attention to the lecturer and relevant content. The bandwidth saving is realized by defining a smaller fixed-size, context-sensitive ‘cropping window’ and generating a new video from the crop regions. The front-end utilises computationally cheap temporal frame differencing at its core: this does not require expensive GPU hardware and also limits the memory required for processing. The VC receives a small set of motion/content bounding boxes and applies established framing heuristics to determine which region to extract from the full 4K frame. Performance results coupled to a user survey show that the system is fit for purpose: it is able to produce good presenter framing/context, over a range of challenging lecture venue layouts and lighting conditions within a time that is acceptable for lecture video processing

A Virtual Cinematographer for Presenter Tracking in 4K Lecture Videos

Lecture recording has become an important part of the provision of accessible tertiary education and having good autonomous recording and processing systems is necessary to make it feasible. In this work, we develop and evaluate a video processing framework that uses 4K video to track the lecturer and frame him/her in a way that simulates a human camera operator. We also investigate general issues pertaining to blackboard usage and its influence on cinematography decisions. We found that post-processing produced better tracking and framing results when compared to some real-time approaches. Furthermore, the entire pipeline can run on a commodity PC and will complete within the suggested time of 300% of the input video length. In fact, our testing showed that 60% of the total processing time can be ascribed to I/O operations. With the removal of redundant reads and writes, this proportion can be reduced. Finally, some algorithms can be remapped to parallel versions which will exploit multicore CPUs or GPUs if these are available