Efficient nonparametric kernel density estimation for real time computer vision
Efficient nonparametric kernel density estimation for real time computer vision
Robust Real-Time Face Detection
International Journal of Computer Vision
Non-Rigid Motion Estimation Using the Robust Tensor Method
CVPRW '04 Proceedings of the 2004 Conference on Computer Vision and Pattern Recognition Workshop (CVPRW'04) Volume 1 - Volume 01
Integral Histogram: A Fast Way To Extract Histograms in Cartesian Spaces
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
Background Subtraction and Shadow Detection in Grayscale Video Sequences
SIBGRAPI '05 Proceedings of the XVIII Brazilian Symposium on Computer Graphics and Image Processing
Robust background subtraction with foreground validation for urban traffic video
EURASIP Journal on Applied Signal Processing
Semantic Event Detection and Classification in Cricket Video Sequence
ICVGIP '08 Proceedings of the 2008 Sixth Indian Conference on Computer Vision, Graphics & Image Processing
Moving object segmentation using the flux tensor for biological video microscopy
PCM'07 Proceedings of the multimedia 8th Pacific Rim conference on Advances in multimedia information processing
Video Tracking: Theory and Practice
Video Tracking: Theory and Practice
Parallel implementation of the integral histogram
ACIVS'11 Proceedings of the 13th international conference on Advanced concepts for intelligent vision systems
Detecting moving objects, ghosts, and shadows in video streams
IEEE Transactions on Pattern Analysis and Machine Intelligence
Median Filtering in Constant Time
IEEE Transactions on Image Processing
Efficient GPU implementation of the integral histogram
ACCV'12 Proceedings of the 11th international conference on Computer Vision - Volume Part I
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Motion detection using background modeling is a widely used technique in object tracking. To meet the demands of real-time multi-target tracking applications in large and/or high resolution imagery fast parallel algorithms for motion detection are desirable. One common method for background modeling is to use an adaptive 3D median filter that is updated appropriately based on the video sequence. We describe a parallel 3D spatiotemporal median filter algorithm implemented in CUDA for many core Graphics Processing Unit (GPU) architectures using the integral histogram as a building block to support adaptive window sizes. Both 2D and 3D median filters are also widely used in many other computer vision tasks like denoising, segmentation, and recognition. Although fast sequential median algorithms exist, improving performance using parallelization is attractive to reduce the time needed for motion detection in order to support more complex processing in multi-target tracking systems, large high resolution aerial video imagery and 3D volumetric processing. Results show the frame rate of the GPU implementation was 60 times faster than the CPU version for a 1K x 1K image reaching 49 fr/sec and 21 times faster for 512 x 512 frame sizes reaching 194 fr/sec. We characterize performance of the parallel 3D median filter for different image sizes and varying number of histogram bins and show selected results for motion detection.