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Multiple moving target tracking method based on BP neural network

A BP neural network, multi-moving target technology, applied in the field of computer vision, can solve the problems of difficult video retrieval, many false positives and false negatives, and a large amount of garbage data.

Inactive Publication Date: 2016-12-07
HUNAN VISION SPLEND PHOTOELECTRIC TECH
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Problems solved by technology

However, the monitoring method is still mainly manual monitoring, which brings many problems, such as fatigue of monitoring personnel, many false positives and missed negatives, difficult video retrieval, a large amount of garbage data, etc., and the real-time performance of the video monitoring system cannot be effectively utilized.

Method used

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  • Multiple moving target tracking method based on BP neural network
  • Multiple moving target tracking method based on BP neural network
  • Multiple moving target tracking method based on BP neural network

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Embodiment Construction

[0076] Now combined with the accompanying drawings Figure 1-4 , taking ordinary multi-target tracking as an example, the details are as follows: the frame difference method is the time difference method, which uses the inter-frame difference of images that are continuous or separated by a certain number of frames to determine the changing area in the image, so as to detect moving objects. Usually, the frame difference method performs difference operation on two consecutive frames of images or multiple frames of images, and then binarizes and filters the differenced images to detect possible moving areas, thereby detecting moving objects.

[0077] The difference map obtained by the frame difference method can be expressed by the following formula:

[0078] D. k (x,y)=|I k+1 (x,y)-I k (x,y)| (1)

[0079]

[0080] where D k (x,y), I k+1 (x,y), I k (x, y) are the inter-frame difference image, the k+1th frame image and the kth frame image respectively. T k (x, y) is th...

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Abstract

The invention discloses a multiple moving target tracking method based on a BP neural network and relates to the field of computer vision technologies. The method comprises the following steps of S1, performing fusion detection to obtain multiple moving targets based on the background difference method and the frame difference method; S2, subjecting a binarization image to further denoising, and inputting the processed image in the BP neural network for multiple target segmentation; and S3, performing multiple moving target tracking based on the BP neural network. The multiple moving target detection mainly comprises three steps of 1) establishing an initialization background model, 2) updating a background using the frame difference method and performing binarization, and 3) performing background difference using the background difference method, and then subjecting the image to binarization. The invention meets requirements for robustness and accuracy of the video image moving target detecting and tracking technology, reduces the computational complexity of neural network global search, and meets requirements for processing speed of the video image moving target detecting and tracking technology.

Description

technical field [0001] The invention relates to the technical field of computer vision, in particular to a multi-moving target tracking method based on a BP neural network. Background technique [0002] With the continuous development and deepening of various policies such as my country's safe city construction, and the continuous enhancement of security awareness of users in various industries such as transportation, education, and finance, the video surveillance market has grown strongly, the number of cameras has increased rapidly, and video resources have exploded. However, the monitoring method is still mainly manual monitoring, which brings many problems, such as fatigue of monitoring personnel, many false positives and missed negatives, difficult video retrieval, a large amount of garbage data, etc., and the real-time performance of the video monitoring system cannot be effectively utilized. [0003] In order to solve the above problems, intelligent video surveillance...

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Application Information

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IPC IPC(8): G06T7/20G06T5/00G06T5/40
CPCG06T5/40G06T2207/20084G06T2207/10016G06T5/70
Inventor 谭树人张斯尧马昊辰
Owner HUNAN VISION SPLEND PHOTOELECTRIC TECH
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