Device and method for realizing moving object identification and track based on FPGA

Abstract

The invention discloses a movement object identification and tracking apparatus based on FPGA and method thereof, whose kernel is an CMOS+ FPGA vision system. The CMOS+FPGA vision system includes a low resolution CMOS vision sensor for capturing continuous image data; and a large-capacity FPGA, which realizes the image characteristic testing algorithm and system control, recognizes and tracks the robot and obstacles in the continuously image, and uses the RS232 serial port to communicate with the host computer. The SOM neural net algorithm is used to execute image processing in the FPGA to obtain the target area position and sizes information, which includes three steps of weight initialization, distance calculation and weight updating. Finally the smooth and consistent velocity information is extracted by noise abatement to the FIR filtering. The information detector has features of small volume, low power consumption and fast process velocity.

Description

technical field [0001] The invention relates to a method for image feature detection using a SOM neural network, in particular to an FPGA-based moving target recognition and tracking device and method thereof. Background technique [0002] 1. FPGA as the core of image processing [0003] In the recognition and tracking of moving targets, since the video signal is used as the processing object, data volume, processing speed and hardware resources are the three mutually restrictive factors for the visual sensor to achieve this goal. Traditionally, there are three ways to achieve image processing. Processor, digital signal processor (DSP) chip, application specific integrated chip (ASIC). There are disadvantages in various approaches: the data processing speed of general-purpose processors is slow and cannot meet the requirements of high-speed processing; DSP has pipeline characteristics and optimized algorithms (MAC only needs one clock cycle), which can accelerate image proc...

AI Technical Summary

Problems solved by technology

There are disadvantages in various approaches: the data processing speed of general-purpose processors is slow and cannot meet the requirements of high-speed processing; DSP has pipeline characteristics and optimized algorithms (MAC only needs one clock cycle), which can accelerate image processing speed, but it is not suitable for all Computing; ASIC has a single function, the sensor has a single function, the cost is expensive, and the adaptability is poor

Using the background difference method usually encounters problems such as background acquisition, background update and background disturbance.

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