Low-power, real-time digital video stabilization using the HyperX parallel processor

Martin A. Hunt; Lin Tong; Keith Bindloss; Shang Zhong; Steve Lim; Benjamin J. Schmid; J. Daniel Tidwell; Paul D. Willson

doi:10.1117/12.884390

5 May 2011 Low-power, real-time digital video stabilization using the HyperX parallel processor

Martin A. Hunt, Lin Tong, Keith Bindloss, Shang Zhong, Steve Lim, Benjamin J. Schmid, J. Daniel Tidwell, Paul D. Willson

Author Affiliations +

Proceedings Volume 8050, Signal Processing, Sensor Fusion, and Target Recognition XX; 80501T (2011) https://doi.org/10.1117/12.884390
Event: SPIE Defense, Security, and Sensing, 2011, Orlando, Florida, United States

Abstract

Coherent Logix has implemented a digital video stabilization algorithm for use in soldier systems and small unmanned air / ground vehicles that focuses on significantly reducing the size, weight, and power as compared to current implementations. The stabilization application was implemented on the HyperX architecture using a dataflow programming methodology and the ANSI C programming language. The initial implementation is capable of stabilizing an 800 x 600, 30 fps, full color video stream with a 53ms frame latency using a single 100 DSP core HyperX hx3100^TM processor running at less than 3 W power draw. By comparison an Intel Core2 Duo processor running the same base algorithm on a 320x240, 15 fps stream consumes on the order of 18W. The HyperX implementation is an overall 100x improvement in performance (processing bandwidth increase times power improvement) over the GPP based platform. In addition the implementation only requires a minimal number of components to interface directly to the imaging sensor and helmet mounted display or the same computing architecture can be used to generate software defined radio waveforms for communications links. In this application, the global motion due to the camera is measured using a feature based algorithm (11 x 11 Difference of Gaussian filter and Features from Accelerated Segment Test) and model fitting (Random Sample Consensus). Features are matched in consecutive frames and a control system determines the affine transform to apply to the captured frame that will remove or dampen the camera / platform motion on a frame-by-frame basis.

Citation Download Citation

Martin A. Hunt, Lin Tong, Keith Bindloss, Shang Zhong, Steve Lim, Benjamin J. Schmid, J. Daniel Tidwell, and Paul D. Willson "Low-power, real-time digital video stabilization using the HyperX parallel processor", Proc. SPIE 8050, Signal Processing, Sensor Fusion, and Target Recognition XX, 80501T (5 May 2011); https://doi.org/10.1117/12.884390

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