A 320mW 342GOPS real-time moving object recognition processor for HD 720p video streams

Abstract

Moving object recognition in a video stream is crucial for applications such as unmanned aerial vehicles (UAVs) and mobile augmented reality that require robust and fast recognition in the presence of dynamic camera noise. Devices in such applications suffer from severe motion/camera blur noise in low-light conditions due to low-sensitivity CMOS image sensors, and therefore require higher computing power to obtain robust results vs. devices used in still image applications. Moreover, HD resolution has become so universal today that even smartphones support applications with HD resolution. However, many object recognition processors and accelerators reported for mobile applications only support SD resolution due to the computational complexity of object recognition algorithms. This paper presents a moving-target recognition processor for HD video streams. The processor is based on a context-aware visual attention model (CAVAM).