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Vehicle detecting algorithm based on intrinsic image decomposition

An intrinsic image and vehicle detection technology, which is applied in the field of intelligent traffic vehicle detection algorithms, can solve problems such as poor results and achieve real-time and accurate detection results

Inactive Publication Date: 2009-10-14
ZHEJIANG UNIV
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  • Abstract
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  • Claims
  • Application Information

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Problems solved by technology

These algorithms are mainly aimed at some specific occasions or a specific application, or the effect is not good
[0006] Generally speaking, the existing video detection systems generally contain two sets of algorithms for day and night respectively, and there is no effective vehicle detection method for day and night.

Method used

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  • Vehicle detecting algorithm based on intrinsic image decomposition
  • Vehicle detecting algorithm based on intrinsic image decomposition
  • Vehicle detecting algorithm based on intrinsic image decomposition

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Embodiment Construction

[0031] Below, the present invention will be further described in conjunction with the drawings and specific embodiments.

[0032] Such as figure 1 As shown, the core part of this algorithm is the extraction of the moving foreground and the decomposition of the intrinsic image. The following two parts are explained in conjunction with the accompanying drawings:

[0033] 1. Extract motion foreground

[0034] This step decomposes the original image sequence for the first time to obtain the background image b(x, y) and the moving foreground part m(x, y, t) in each frame image. For the specific flow chart, see figure 2 .

[0035] First, convert the input image sequence {i(t-n×Δt), n=0, 1, 2,...k} to the logarithmic domain (k=9 in the algorithm, the interval Δt can be adjusted by yourself), and then by f x =[0,1,-1] T and f y =[0, 1, -1] for derivation filtering, get:

[0036] i x / y (x, y, t) = f x / y *i(x,y,t)

[0037] i x / y (x, y, t) = f x / y *[b(x,y)+m(x,y,t)]

[0038]...

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Abstract

The invention relates to a vehicle detecting algorithm in an intelligent traffic system, in particular to a vehicle detecting algorithm based on intrinsic image decomposition, which is universal at day and night. The specific practice thereof comprises the steps of: at first, carrying out derivation and filter to an initial input image on a log-domain to convert the initial input image to a gradient domain; then calculating the difference between the gradient map of a current image and the gradient map of a background image on the gradient domain to obtain the gradient map of a moving foreground image; using the method of intrinsic image decomposition to process the gradient map of the moving foreground image to obtain the gradient map of a photogram and the gradient map of a target image; extracting the pixels with the gradient amplitude larger than a threshold T in the gradient map of the target image and using the pixels as moving target points; and aiming at a certain region, considering that the vehicle passes through the region if the number of the moving target points exceeds a certain proportion of the total number of the pixels in the region. The vehicle detecting algorithm based on intrinsic image decomposition has the beneficial effects of removing the effects of shadows and illumination and being capable of accurately carrying out detection to the vehicles at day and night in real time.

Description

technical field [0001] The invention relates to an intelligent traffic vehicle detection algorithm, in particular to a day and night universal intelligent traffic vehicle detection technology based on intrinsic image decomposition. Background technique [0002] With the development of Intelligent Transportation System (ITS), the vehicle detection algorithm as its core technology has become an important research direction in computer vision. Whether the accurate outline of the vehicle can be obtained is very important for subsequent target classification and tracking, and it also affects the accuracy of the entire system. [0003] Vehicle detection in shaded conditions and at night has always been a difficult and hot topic of research. In daytime vehicle detection, shadows are the main disturbance. At night, vehicle detection is not only disturbed by shadows, but also affected by lights. First of all, the headlights with strong light are enough to cause changes in the ligh...

Claims

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Application Information

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IPC IPC(8): G08G1/017G06T7/20
Inventor 于慧敏王婷吴嘉
Owner ZHEJIANG UNIV
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