Depth from motion algorithm and hardware architecture for smart cameras

Abstract : Applications such as autonomous navigation, robot vision, autonomous flying, etc., require 1 depth map information of the scene. Depth can be estimated by using a single moving camera 2 (depth from motion). However, traditional depth from motion algorithms have low processing speed 3 and high hardware requirements that limits the embedded capabilities. In this work, we propose 4 a hardware architecture for depth from motion that consists of a flow/depth transformation and 5 a new optical flow algorithm. Our optical flow formulation consists in an extension of the stereo 6 matching problem. A pixel-parallel/window-parallel approach where a correlation function based in 7 the Sum of Absolute Differences computes the optical flow is proposed. Further, in order to improve 8 the Sum of Absolute Differences performance, the curl of the intensity gradient as preprocessing step 9 is proposed. Experimental results demonstrated that it is possible to reach higher accuracy (90% of 10 accuracy) compared with previous FPGA-based optical flow algorithms. For the depth estimation, 11 our algorithm delivers dense maps with motion and depth information on all the image pixels, with 12 a processing speed up to 128 times faster than previous works and making it possible to achieve high 13 performance in the context of embedded applications. 14
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Contributeur : Abiel Aguilar-González <>
Soumis le : dimanche 23 décembre 2018 - 21:51:06
Dernière modification le : jeudi 7 février 2019 - 15:37:00


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  • HAL Id : hal-01964830, version 1


Abiel Aguilar-González, Miguel Arias-Estrada, François Berry. Depth from motion algorithm and hardware architecture for smart cameras. Sensors, MDPI, 2018. 〈hal-01964830〉



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