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Accurate optical flow estimation is a crucial task for many computer vision applications. However, because of its computational power and processing speed requirements, it is rarely used for real-time obstacle detection, especially for small unmanned vehicle and embedded applications. Two hardware-friendly vision algorithms are proposed in this paper to address this challenge. A ridge regression-based optical flow algorithm is developed to cope with the existing collinear problem in traditional least-squares approaches for calculating optical flow. Additionally, taking advantage of hardware parallelism, spatial and temporal smoothing operations are applied to image sequence derivatives to improve accuracy. An efficient motion field analysis algorithm using the optical flow values and based on a simplified motion model is also developed and implemented in hardware. The resulting obstacle detection algorithm is specifically designed for ground vehicles moving on planar surfaces. Results from the software simulations and hardware execution of the two proposed algorithms prove that with adequate hardware, a low power, compact obstacle detection sensor can be realized for small unmanned vehicles and embedded applications.