This paper presents a brief survey on coarse grained Reconfigurable architectures for motion estimation. The motion estimation processor demands a very large amount of computing power in recent video coding standard H.264/AVC. To reduce computational complexity without affecting image quality, Motion estimation algorithms are implemented on various course grained Reconfigurable architectures such as Morphosys, Matrix, Rapid, Mora and Chess. The purpose of this study is to compare these architectures based on Granularity, resource utilization, memory bandwidth and computation model. The methodology and results presented here provide useful guidelines to system designers in selecting coarse grained Reconfigurable Architectures for motion estimation