Image-based bus passenger number automatic statistics meter

Abstract

The invention discloses an image-based automatic bus number counting instrument, which includes an image acquisition and processing module. The image acquisition and processing module includes a CCD camera, a video decoder chip, a programmable logic chip and a DSP chip; the output of the CCD camera is connected to the input of the video decoder chip, and the control and output terminals of the video decoder chip are connected to the programmable logic chip, which can be programmed The logic chip is connected with the DSP chip; the programmable logic chip includes a hardware description language program for realizing data exchange, control and buffering between the video decoding chip and the DSP chip; the image processing and recognition software runs on the DSP chip. The invention adopts high-speed DSP and FPGA design, and performs image recognition through advanced image processing and recognition algorithms. The system has fast operation speed and can realize real-time and accurate recognition of people; its hardware structure is simple, its performance is stable, and its installation and debugging are convenient.

Description

technical field [0001] The invention relates to an embedded digital processing circuit and an image processing algorithm, in particular to an image recognition and application technology based on a DSP digital processing circuit. Background technique [0002] The technologies currently used for bus passenger flow statistics mainly include: photoelectric detection method, pressure sensing method, photoelectric sensing method, IC card reading method and video analysis method. [0003] The photoelectric detection method requires the shape of the object to be detected to be standardized and uniform, and the motion behavior to be strictly consistent. When used in the detection of bus passenger flow, there are many factors to consider. For example: the individual differences of passengers are very large, different in height, fat and thin, and the actions of passengers getting on the bus are also different. In addition, it is possible to carry things by hand, which is easy to cause...

AI Technical Summary

Problems solved by technology

The statistical accuracy of the photoelectric sensing method can reach 92%-93% during the normal passenger flow period, but the statistical accuracy drops to 83%-85% during the peak passenger flow period, especially the statistical delay during the peak passenger flow period has a great impact on the operating efficiency of the entire system great influence

The video analysis method is complex in structure, expensive and difficult to implement

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