Block-Matching Optical Flow for Dynamic Vision Sensor- Algorithm and FPGA Implementation
Rapid and low power computation of optical flow (OF) is potentially useful in robotics. The dynamic vision sensor (DVS) event camera produces quick and sparse output, and has high dynamic range, but conventional OF algorithms are frame-based and cannot be directly used with event-based cameras. Previous DVS OF methods do not work well with dense textured input and are designed for implementation in logic circuits. This paper proposes a new block-matching based DVS OF algorithm which is inspired by motion estimation methods used for MPEG video compression. The algorithm was implemented both in software and on FPGA. For each event, it computes the motion direction as one of 9 directions. The speed of the motion is set by the sample interval. Results show that the Average Angular Error can be improved by 30\% compared with previous methods. The OF can be calculated on FPGA with 50\,MHz clock in 0.2\,us per event (11 clock cycles), 20 times faster than a Java software implementation running on a desktop PC. Sample data is shown that the method works on scenes dominated by edges, sparse features, and dense texture.
Code (0)
등록된 구현이 없습니다.
Tasks
Motion EstimationOptical Flow EstimationVideo CompressionMethods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
ABMOF: A Novel Optical Flow Algorithm for Dynamic Vision Sensors
Dynamic Vision Sensors (DVS), which output asynchronous log intensity change events, have potential applications in high-speed robotics, autonomous cars and drones. The precise event timing, sparse output, and wide dynam…
Optical Flow EstimationUnsupervised Learning Optical Flow in Multi-frame Dynamic Environment Using Temporal Dynamic Modeling
For visual estimation of optical flow, a crucial function for many vision tasks, unsupervised learning, using the supervision of view synthesis has emerged as a promising alternative to supervised methods, since ground-t…
Optical Flow EstimationLearning Optical Expansion From Scale Matching
This paper address the problem of optical expansion (OE). OE describes the object scale change between two frames, widely used in monocular 3D vision tasks. Previous methods estimate optical expansion mainly from opt…
Optical Flow EstimationTransFlow: Transformer as Flow Learner
Optical flow is an indispensable building block for various important computer vision tasks, including motion estimation, object tracking, and disparity measurement. In this work, we propose TransFlow, a pure transformer…
Motion Estimationobject-detectionObject DetectionObject Tracking+3Learning Dynamic Point Cloud Compression via Hierarchical Inter-frame Block Matching
3D dynamic point cloud (DPC) compression relies on mining its temporal context, which faces significant challenges due to DPC's sparsity and non-uniform structure. Existing methods are limited in capturing sufficient tem…
Feature CorrelationMotion CompensationMotion EstimationOptical Flow Estimation+1