视觉目标跟踪：GM-PHD滤波器与特征测量法

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"本文提出了一种新型的概率假设密度(PHD)滤波器，用于视觉目标跟踪，特别是在有多个移动目标的场景中。该方法利用高斯混合(GM)PHD滤波器，通过特征测量来近似随机目标集合的后验强度。采用蒙特卡洛技术，通过采样粒子来获取带有整数标签的特征测量随机集，并通过自适应权重融合颜色和边缘特征，以提高跟踪目标的表征能力。实验结果证明了该方法的有效性。关键词包括概率假设密度滤波器、特征测量、视觉跟踪和蒙特卡洛技术。" 详细说明: 1. 概率假设密度(PHD)滤波器：这是一种用于多目标跟踪的实用方法，基于随机有限集理论。它不同于最优贝叶斯多目标滤波器，主要关注目标数量的不确定性，通过传播目标集合的后验强度来进行目标状态估计。 2. 高斯混合(GM)PHD滤波器：在本论文中，作者使用GM模型来扩展PHD滤波器，通过将后验分布表示为高斯混合模型，可以更好地处理目标状态的多样性，提高跟踪的准确性。 3. 特征测量：为了在视觉序列中获取目标的PHD，论文提出了一种方法，即通过特征（如颜色和边缘）来近似后验强度。这有助于区分不同的目标，并提高在复杂背景下的跟踪性能。 4. 蒙特卡洛技术：在处理随机集的采样过程中，采用了蒙特卡洛方法。这涉及使用随机样本来模拟和估计目标的运动轨迹，从而获得目标的特征测量。 5. 整数标签样本粒子：每个粒子代表一个可能的目标状态，通过赋予它们整数标签，可以有效地跟踪和区分多个目标。 6. 自适应权重融合：为了提高跟踪目标的表征能力，论文中采用了一种自适应权重机制，将颜色和边缘特征融合在一起。这允许滤波器根据环境变化动态调整对不同特征的重视程度。 7. 应用领域：视觉目标跟踪在多个领域都有重要应用，如道路交通控制、医学图像序列分析、视频压缩、安全监控系统等。 8. 数据关联问题：在视觉跟踪中，数据关联是一个关键挑战，指的是如何正确地将传感器观测与实际存在的目标匹配。PHD滤波器提供了一种有效处理这个问题的方法。该研究论文介绍了一种创新的PHD滤波器变体，通过特征测量和自适应权重融合优化了视觉目标跟踪，尤其在有多个移动目标的情况下表现出色。通过蒙特卡洛技术和高斯混合模型，该方法能够处理不确定性，提高了在复杂环境中的跟踪性能。

Abstract—The probability hypothesis density (PHD) filter is

a practical alternative to the optimal Bayesian multiple targets

filter based on random finite sets (RFS). It propagates the

posterior intensity of the random sets of targets. In this paper,

we apply the Gaussian Mixture (GM) PHD filter to track a

random number of moving targets in visual sequences. To

obtain the PHD of visual objects, we propose a method to

approximate the posterior intensity using the feature

measurement. Monte Carlo technology is adopted to obtain

the feature measurement random set by sample particles with

the integer label. And we adopt an adaptive weight to fuse the

color and edge features to improve the represent ability of

tracking targets. The experimental results have demonstrated

the effectiveness of our method.

Keywords: probability hypothesis density filter, feature

measurement, visual tracking,Monte Carlo technology

I. INTRODUCTION

isual objects tracking is an important task in

many applications, such as road traffic control,

medical image sequence analysis, video

compression, security, and surveillance systems.

The data association problem in multiple targets

tracking usually involves ensuring that the correct

measurement is given to each stochastic filter so that the

trajectories of each target can be accurately estimated.

This is referred to as measurement-to-track association.

The three classical approaches to this are the nearest

neighbor (NN) method [1], the Joint Probabilistic Data

Association Filter (JPDAF) [1], and the Multiple

Hypothesis Tracking filter (MHT) [2].

The NN filter simple takes the nearest validated

measurement to the predicted measurement to update

each of the target states. This can result in problems

since the nearest validated measurement may be the

same for two different targets. The JPDA method takes

into account the fact that a measurement may fall inside

the intersection of two or more validation gates of

several different targets and so could have originated

from any of these targets or form clutter. The ideal MHT

filter maintains probabilities of all possible associations

at each time step. This does not just consider the

probabilities from the previous time step, which allows

for backtracking and also track initiation. In practice, it is

not feasible to keep track of all possible hypotheses, as

the computational complexity grows exponentially [3].

An alternative solution to the multiple targets tracking

problem is to view the set of observations collectively,

and try to estimate the set of target states directly, where

the correct report-to-track association is considered

unobservable [4]. The disadvantage of this approach is

that the continuity of the individual target tracks is not

kept. One such method uses Finite Set Statistics for

multiple target tracking [5], with an approach analogous

to the recursion used in Bayesian filtering by

constructing multiple target posterior distributions.

This paper is organized as follows. Section 2 provides

an overview of probability hypothesis density filter and

the Gaussian Mixture implementation. Extracting feature

measurement set and selected feature are described in

section 3. Section 4 provides the experiment results and

performance evaluation. Finally, Section 5 concludes the

paper.

II. BACKGROUND

Multi-sensor multi-target tracking is a class of dynamic

state estimation problems in which the entity of interest is a

set that is random in the number of elements as well as the

values of individual elements [6]. Finite random sets are

therefore natural and intuitive representations of multi-target

states and multi-target measurements. The modelling of

multi-target dynamic using random sets naturally leads to

tracking algorithms which incorporate track initiation, a

procedure that has mostly been performed separately in

traditional tracking algorithms. Bayesian multi-target

tracking (in the random finite set framework) propagates the

multi-target posterior density recursively in time. This

involves the evaluation of multiple (set) integrals and the

computational intractability is more severe than its

single-target counterpart [7]. Detailed description on the

multi-target Bayes recursion is presented in literature [8].

A. PHD filter

The PHD filter is an approximation developed to

alleviate the computational intractability in the

multi-target Bayes filter. Instead of propagating the

multi-target posterior density in time, the PHD filter

propagates the posterior intensity, a first-order statistical

A novel probability hypothesis density filter for tracking visual

targets

Xiaofeng LU

, Xinhong Hei

, Lei Wang

, Haiyan Jin

The School of Computer Science and Engineering

Xi’an University of Technology

luxiaofeng@xaut.edu.cn

103

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