Real-Time Stereo Vision System: A Multi-Block Matching on GPU

Real-time stereo vision is attractive in many areas such as outdoor mapping and navigation. As a popular accelerator in the image processing field, GPU is widely used for the studies of the stereo vision algorithms. Recently, many stereo vision systems on GPU have achieved low error rate, as a result of the development of deep learning. However, their processing speed is normally far from the real-time requirement. In this paper, we propose a real-time stereo vision system on GPU for the high-resolution images. This system also maintains a low error rate compared with other fast systems. In our approach, the image is resized to reduce the computational complexity and to realize the real-time processing. The low error rate is kept by using the cost aggregation with multiple blocks, secondary matching and sub-pixel estimation. Its processing speed is 41 fps for 2888×1920 pixels images when the maximum disparity is 760

An energy-efficient parallel multi-core ADAS processor with robust visual attention and workload-prediction DVFS for real-time HD stereo stream

A heterogeneous multicore processor is proposed to accelerate advanced driver assistance system (ADAS). To enable a real-time operation of ADAS functions with 720p stereo video stream, multiple granualrity parallel SIMD/MIMD architecture is proposed with precise visual attention and high throughput network-on-chip to reduce computation cost and network congestion, respectively. In addition, it employs a data resource management processor to control workload-prediction dynamic voltage and frequency scaling to reduce power consumption. As a result, the proposed SoC ahcieves 862GOPS/W energy efficiency and 31.4GOPS/mm2 area efficiency, which are 53% and 75% improvement over the state-of-the-art ADAS processor, respectively