Superpipelined high-performance optical-flow computation architecture
Computer Vision and Image Understanding
Optimization strategies for high-performance computing of optical-flow in general-purpose processors
IEEE Transactions on Circuits and Systems for Video Technology
Fine grain pipeline architecture for high performance phase-based optical flow computation
Journal of Systems Architecture: the EUROMICRO Journal
Real-time embedded system for rear-view mirror overtaking car monitoring
SAMOS'06 Proceedings of the 6th international conference on Embedded Computer Systems: architectures, Modeling, and Simulation
Two algorithms for motion estimation from alternate exposure images
Proceedings of the 2010 international conference on Video Processing and Computational Video
On line background modeling for moving object segmentation in dynamic scenes
Multimedia Tools and Applications
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Recent advances in imaging sensor technology make high frame-rate video capture practical. As demonstrated in previous work, this capability can be used to enhance the performance of many image and video processing applications. The idea is to use the high frame-rate capability to temporally oversample the scene and, thus, to obtain more accurate information about scene motion and illumination. This information is then used to improve the performance of image and standard frame-rate video applications. This paper investigates the use of temporal oversampling to improve the accuracy of optical flow estimation (OFE). A method for obtaining high accuracy optical flow estimates at a conventional standard frame rate, e.g., 30 frames/s, by first capturing and processing a high frame-rate version of the video is presented. The method uses the Lucas-Kanade algorithm to obtain optical flow estimates at a high frame rate, which are then accumulated and refined to estimate the optical flow at the desired standard frame rate. The method demonstrates significant improvements in OFE accuracy both on synthetically generated video sequences and on a real video sequence captured using an experimental high-speed imaging system. It is then shown that a key benefit of using temporal oversampling to estimate optical flow is the reduction in motion aliasing. Using sinusoidal input sequences, the reduction in motion aliasing is identified and the desired minimum sampling rate as a function of the velocity and spatial bandwidth of the scene is determined. Using both synthetic and real video sequences, it is shown that temporal oversampling improves OFE accuracy by reducing motion aliasing not only for areas with large displacements but also for areas with small displacements and high spatial frequencies. The use of other OFE algorithms with temporally oversampled video is then discussed. In particular, the Haussecker algorithm is extended to work with high frame-rate sequences. This extens- - ion demonstrates yet another important benefit of temporal oversampling, which is improving OFE accuracy when brightness varies with time.