Abstract

Dynamic target recognition is an important issue in the field of image processing research. It is widely used in photoelectric detection, target tracking, video surveillance areas. Complex cruise scene of target detection, compared to the static background, since the target and background objects together and both are in motion, greatly increases the complexity of moving target detection and recognition. Based on the practical engineering applications, combining an embedded systems and real-time image detection technology, this paper proposes a real-time movement detection method on an embedded system based on the FPGA + DSP system architecture on an embedded system. The DSP digital image processing system takes high speed digital signal processor DSP TMS320C6416T as the main computing components. And we take large capacity FPGA as coprocessor. It is designed and developed a high-performance image processing card. The FPGA is responsible for the data receiving and dispatching, DSP is responsible for data processing. The FPGA collects image data and controls SDRAM according to the digital image sequence. The SDRAM realizes multiport image buffer. DSP reads real-time image through SDRAM and performs scene motion detection algorithm. Then we implement the data reception and data processing parallelization. This system designs and realizes complex cruise scene motion detection for engineering application. The image edge information has the anti-light change and the strong anti-interference ability. First of all, the adjacent frame and current frame image are processed by convolution operation, extract the edge images. Then we compute correlation strength and the value of movement offset. We can complete scene motion parameters estimation by the result, in order to achieve real-time accurate motion detection. We use images in resolution of 768 576 and 25Hz frame rate to do the real-time cruise experiment. The results show that the proposed system achieves real-time processing requirements for the engineering applications. 2014 SPIE.

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