Bit Rate Control for Real-time Multipoint Video Conferencing

With the rapid development of video compression and network technology, real-time video communications has become a popular part of our daily life. Rate control is needed to satisfy the expectation of high quality and to make it possible to transmit over limited bandwidth. The objective of this thesis is to design a rate control scheme for a real-time Transcoding-Compositing Multipoint Video Conferencing System, which operates exclusively in the DCT domain. In this Transcoding-Compositing system, the mode of the composited frame should firstly be decided before encoding the composited image. A mode decision method relying on Karhunen-Loeve scene change detection is proposed. A new linear source Rate-Distortion model is developed in the - domain ( is the percentage of zero), based on which rate control scheme is designed. The designed rate control scheme is parted into three levels: Frame Level, Sub-frame Level, and Macroblock Level. Frame Level rate control decides the bit budget for each frame based on the buffer fullness. Sub-frame Level rate control optimizes the distribution of the bit budget among the decimated sub-images. Based on the linear source model, Macroblock Level rate control carries out an adaptive procedure to precisely control the number of encoding bits for each sub-image

Event-based Vision: A Survey

Event cameras are bio-inspired sensors that differ from conventional frame cameras: Instead of capturing images at a fixed rate, they asynchronously measure per-pixel brightness changes, and output a stream of events that encode the time, location and sign of the brightness changes. Event cameras offer attractive properties compared to traditional cameras: high temporal resolution (in the order of microseconds), very high dynamic range (140 dB vs. 60 dB), low power consumption, and high pixel bandwidth (on the order of kHz) resulting in reduced motion blur. Hence, event cameras have a large potential for robotics and computer vision in challenging scenarios for traditional cameras, such as low-latency, high speed, and high dynamic range. However, novel methods are required to process the unconventional output of these sensors in order to unlock their potential. This paper provides a comprehensive overview of the emerging field of event-based vision, with a focus on the applications and the algorithms developed to unlock the outstanding properties of event cameras. We present event cameras from their working principle, the actual sensors that are available and the tasks that they have been used for, from low-level vision (feature detection and tracking, optic flow, etc.) to high-level vision (reconstruction, segmentation, recognition). We also discuss the techniques developed to process events, including learning-based techniques, as well as specialized processors for these novel sensors, such as spiking neural networks. Additionally, we highlight the challenges that remain to be tackled and the opportunities that lie ahead in the search for a more efficient, bio-inspired way for machines to perceive and interact with the world

Illumination invariant stationary object detection

A real-time system for the detection and tracking of moving objects that becomes stationary in a restricted zone. A new pixel classification method based on the segmentation history image is used to identify stationary objects in the scene. These objects are then tracked using a novel adaptive edge orientation-based tracking method. Experimental results have shown that the tracking technique gives more than a 95% detection success rate, even if objects are partially occluded. The tracking results, together with the historic edge maps, are analysed to remove objects that are no longer stationary or are falsely identified as foreground regions because of sudden changes in the illumination conditions. The technique has been tested on over 7 h of video recorded at different locations and time of day, both outdoors and indoors. The results obtained are compared with other available state-of-the-art methods