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Multi-vehicle video tracking method based on color space information

A color space and video tracking technology, applied in CCTV systems, traffic flow detection, etc., can solve problems such as high computational complexity and a large number of particles

Inactive Publication Date: 2011-02-16
BEIHANG UNIV
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  • Abstract
  • Description
  • Claims
  • Application Information

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Problems solved by technology

In particular, Zhao Q et al. published a paper titled "Object tracking using color correlogram" (Object tracking using color correlogram) and a paper titled "Simplified color correlogram for motion appearance analysis in video tracking" (Motion Observability analysis of the simplified color correlogram for visual tracking) documents respectively give the MeanShift frame tracking method based on color correlation, but when calculating the color correlogram, the computational complexity of the method is high
Particle filter is widely used due to its nonlinear, non-Gaussian and multi-modal characteristics, but purely using particle filter for tracking requires a large number of particles

Method used

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  • Multi-vehicle video tracking method based on color space information

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Embodiment Construction

[0083] Such as figure 1 Shown is a flowchart of the method of the present invention, and the method includes the following steps:

[0084] (1) Detect vehicle movement area

[0085] 1. Self-adaptive estimation of highway monitoring lane area

[0086] Suppose that in the (k+1)th frame, the gray value of the background pixel p is expressed as:

[0087] G(k+1,p)=G(k,p)+L(k,p)+noise 1 (k, p)

[0088] Among them, G(k, p) is the gray value of the background pixel p in the k-th frame; L(k, p) is an uncertain model describing the change of illumination over time; noise 1 (k, p) is Gaussian white noise centered at zero (including system measurement error); the gray scale of input image pixel p is expressed as:

[0089] I(k,p)=G(k,p)+noise 2 (k, p)

[0090] Among them, noise 2 (k, p) is a Gaussian white noise centered on zero; eliminate the system measurement error to get:

[0091] I(k+1,p)=G(k,p)+ω(k+1,p)

[0092] Among them, ω(k+1,p)=L(k,p)+noise 1 (k, p)+noise 2 (k+1, p). ω(n,p) is a Gaussian dis...

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Abstract

The invention relates to a multi-vehicle video tracking method based on color space information. The method is characterized by comprising the following steps of: (1) detecting a vehicle motion region: carrying out self-adaptive estimation on a monitored lane region of a highway and detecting a vehicle target region by adopting a rapid constrained triangulation method; (2) establishing a vehicle presentation model: carrying out segmenting treatment on vehicles according to the symmetry characteristics of the vehicles and establishing color related map characteristic vectors; (3) establishing a vehicle state model: establishing a multi-vehicle state model and predicting states by adopting secondary linear regression as a basis; and (4) positioning a plurality of vehicles based on particle filtration. According to the invention, the vehicle presentation model is established by utilizing a space incidence relation for quantizing colors and combining a segmenting method, and the robust tracking of the plurality of vehicles can be realized.

Description

Technical field [0001] The invention relates to a multi-vehicle video tracking method based on color space information, and belongs to the technical field based on machine vision. Background technique [0002] Under normal circumstances, the monitoring of traffic scenes is mainly done through manual control. It requires a lot of manpower and material resources. Even so, in a high-intensity work environment, omissions may still occur; especially when an abnormal vehicle event occurs, it cannot be further quickly responded. Therefore, Intelligent Transportation Systems (ITS) has been developed in recent years based on machine vision. It detects, recognizes, and tracks vehicle targets by analyzing video sequences to obtain motion parameters such as position, speed, direction, and acceleration, without any human intervention or very little human intervention. [0003] Zehang Sun et al. published an article "Road Vehicle Detection: An Overview" ("IEEE Transactions on Pattern Analysis ...

Claims

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Application Information

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IPC IPC(8): H04N7/18G08G1/01
Inventor 魏奇李超熊璋
Owner BEIHANG UNIV
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