A Run-time Performance and Power Optimization of Parallel Disparity Estimation on Many-Core Platforms
نویسندگان
چکیده
Stereo vision has become more pervasive in embedded and physically-constrained systems. Disparity estimation (DE) algorithms are used in stereo vision to calculate the depth of objects in a scene. They are used in such applications as video surveillance, autonomous vehicles and mobile robots [Cyganek and Siebert 2009]. Algorithms need to satisfy real-time performance demands, with high matching precision and low power consumption. The choice of estimation algorithm and implementation platform are both important factors to meet these constraints and produce a viable embedded stereo matching system. With the appearance of stereo vision algorithms in dynamic environments, where constraints can change frequently, achieving run-time power scalability without sacrificing real-time performance has emerged as the next challenge in this domain. For example, in autonomous vehicles the performance requirement may be driven
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