Abstract
The purpose of this study is to develop a motion sensor (delivering optical flow estimations) using a platform that includes the sensor itself, focal plane processing resources, and co-processing resources on a general purpose embedded processor. All this is implemented on a single device as a SoC (System-on-a-Chip). Optical flow is the 2-D projection into the camera plane of the 3-D motion information presented at the world scenario. This motion representation is widespread well-known and applied in the science community to solve a wide variety of problems. Most applications based on motion estimation require work in real-time; hence, this restriction must be taken into account. In this paper, we show an efficient approach to estimate the motion velocity vectors with an architecture based on a focal plane processor combined on-chip with a 32 bits NIOS II processor. Our approach relies on the simplification of the original optical flow model and its efficient implementation in a platform that combines an analog (focal-plane) and digital (NIOS II) processor. The system is fully functional and is organized in different stages where the early processing (focal plane) stage is mainly focus to pre-process the input image stream to reduce the computational cost in the post-processing (NIOS II) stage. We present the employed co-design techniques and analyze this novel architecture. We evaluate the system’s performance and accuracy with respect to the different proposed approaches described in the literature. We also discuss the advantages of the proposed approach as well as the degree of efficiency which can be obtained from the focal plane processing capabilities of the system. The final outcome is a low cost smart sensor for optical flow computation with real-time performance and reduced power consumption that can be used for very diverse application domains.
Highlights
The term Optical Flow refers to the visual phenomenon due to the apparent movement perceived when we move through a scene and/or regarding the objects moving within it
Following the results of [8,15,16], we focus on Lucas and Kanade’s optical flow method [2], which has been highlighted by the mentioned contributions as a good trade-off between accuracy and performance
In the results shown below we can observe that the optical flow increases, as we increase the sparse integration block (5 × 5 Sparse Integration Block (SIB) and 9 × 9 SIB)
Summary
The term Optical Flow refers to the visual phenomenon due to the apparent movement perceived when we move through a scene and/or regarding the objects moving within it. It represents the projection of the 3-D motion presented in the scene to the 2-D plane of the image sensor or the retina. Note that as a consequence of this protection, depth information is partially lost and the estimation of the 3-D scene structure and motion from the available 2-D field is a very complex task. The most widely used ones are the gradient based methods. These methods are based on the constant-brightness assumption. An extended model is the well-known local method proposed by
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