2014 Sixth International Symposium on Parallel Architectures, Algorithms and Programming (PAAP) (2014)
July 13, 2014 to July 15, 2014
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/PAAP.2014.64
In recent years, due to frequent accidents in mine, it is significantly meaningful to develop monitoring algorithms that can improve safeties. However, the video signal in mine is usually strongly polluted during its sampling and transmission, resulting in difficulties for monitoring. This study presents a mine safety monitoring algorithm. Our approach has two parts, i.e., preprocessing and recognition of visual information. First, we address a video enhancement method that uses inter-frame similarity prediction to accelerate non-local means, second, we propose an object recognition method based on improved Hough forest, it speeds up recognition process by reducing the size of search window, and it increases the recognition accuracy through introducing time-dimensional analysis. Experimental results illustrate that the proposed algorithm obtains 66% reduction in denoising time and an improved recognition rate. It is reasonable and reliable to apply our algorithm to mine safety monitoring.
Noise reduction, Three-dimensional displays, Object detection, Vectors, Monitoring, Computer vision, Algorithm design and analysis,vedio denoising, 3D non-local means, Hough forest, object recognition
Bo Fu, Chuan-Ming Song, "Object Enhancement and Recognition Based on Hough Forest for Underground Video", 2014 Sixth International Symposium on Parallel Architectures, Algorithms and Programming (PAAP), vol. 00, no. , pp. 75-80, 2014, doi:10.1109/PAAP.2014.64