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Target tracking method based on kernel density estimation

A kernel density estimation and target tracking technology, applied in the field of computer vision, can solve problems such as large amount of calculation and slow speed, and achieve the effect of overcoming influence, strong robustness and high accuracy

Inactive Publication Date: 2018-05-22
HUNAN VISION SPLEND PHOTOELECTRIC TECH
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Problems solved by technology

The particle filter algorithm is an open system based on the dynamic state space model (DSSM), and the tracking effect is stable, but the calculation is large and the speed is slow

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  • Target tracking method based on kernel density estimation
  • Target tracking method based on kernel density estimation
  • Target tracking method based on kernel density estimation

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Embodiment Construction

[0031] The present invention provides a target tracking method based on kernel density estimation. In order to adapt to the change of target shape in the visual tracking process, the kernel density estimation is used to model the color distribution of moving targets in a video sequence, and the CamShift algorithm is used for tracking. The invention can realize stable tracking of targets under different motion conditions, overcomes the influence of scale changes on tracking, and is a tracking algorithm with strong robustness.

[0032] The specific embodiments of a target tracking method based on kernel density estimation of the present invention will be described in further detail below in conjunction with the accompanying drawings in the specification of the present application. Obviously, the described embodiments are only a part of the embodiments of the present invention, not all of them. Embodiments, based on the embodiments of the present invention, all other embodiments obta...

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Abstract

The invention discloses a target tracking method based on kernel density estimation, wherein the method relates to the field of computer vision. The method comprises the following steps of firstly foradapting target form change in a vision tracking process, establishing a color distribution model for an H component of the target in an HSV space by means of kernel density estimation, so that tracking can be accurately finished on the condition that local shielding of the target occurs, and then performing target tracking by means of a CamShift algorithm. The target tracking method is an algorithm with relatively high robustness and is suitable for the environment which has high background change stability, single color and color distribution which is over-complicated. Compared with a gray-based template matching method, the target tracking method has higher accuracy.

Description

Technical field [0001] The invention relates to the field of computer vision, in particular to a target tracking method based on kernel density estimation. Background technique [0002] Visual tracking is a research hotspot in the field of intelligent surveillance. It refers to the process of detecting, extracting, identifying and tracking moving targets in a video sequence to obtain target motion parameters and motion trajectories. At present, the mainstream tracking methods include MeanShift algorithm and particle filter algorithm. When the MeanShift algorithm is used to track the target, the mean vector sometimes converges to the local optimum of the Bhatacharyya coefficient surface, which makes the tracking invalid. The particle filter algorithm is an open system based on the Dynamic State Space Model (DSSM). The tracking effect is stable, but the calculation is large and the speed is slow. [0003] In order to reduce the amount of calculation, improve the real-time performan...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06T7/246G06T7/90G06T7/66
CPCG06T7/251G06T7/66G06T7/90G06T2207/10016
Inventor 颜微
Owner HUNAN VISION SPLEND PHOTOELECTRIC TECH
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