积分直方图在稳健碎片跟踪中的应用

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"这篇文章介绍了一种名为‘Frag-Track’的新型目标跟踪算法，它基于图像碎片（或补丁）并使用积分直方图来处理目标的部分遮挡情况。与传统的模型基部分（如人体追踪中的肢体和躯干）不同，此方法中的补丁是任意的，不依赖于对象模型。每一片段通过对当前帧中对应区域的直方图进行比较，投票决定可能的目标位置和尺度。然后，通过最小化鲁棒统计量来结合多个补丁的投票地图，以实现对目标位置的精确估计。积分直方图数据结构在这里起到了关键作用，它能高效地提取图像中多个矩形区域的直方图，从而在处理跟踪问题时显著提高效率。" 详细内容： "在目标跟踪领域，Frag-Track算法提供了一种创新的解决方案，尤其对于处理目标部分被遮挡的情况表现出强大的适应性。算法的核心在于将模板对象分解为多个独立的图像碎片或补丁，这些补丁不依赖于预定义的对象模型，而是可以任意选取。这种方法的好处在于增加了灵活性，使得算法能够在目标形状变化或部分被遮挡时仍能有效地跟踪。每个图像补丁都有一个对应的直方图，用于描述其颜色和纹理特性。在每一帧中，每个补丁都会根据其直方图与当前帧中的相应区域进行比较，以此来预测目标可能出现的位置和大小。这种比较过程利用了积分直方图这一高效的数据结构，它可以快速计算出图像区域内任意形状的直方图，大大减少了计算时间。接下来，Frag-Track算法通过最小化一种鲁棒统计量来整合所有补丁的投票结果。这样的统计量设计可以抵御噪声和异常值的影响，确保在复杂环境下也能得到稳定的结果。最终，这个优化过程确定了最有可能的目标位置，从而实现精确的跟踪。 Frag-Track算法利用积分直方图和鲁棒统计方法，克服了传统目标跟踪算法在处理遮挡和部分可见性问题上的局限，提升了跟踪的稳定性和准确性。这一技术在视频监控、自动驾驶、无人机导航等领域有着广泛的应用潜力。"

Robust Fragments-based Tracking using the Integral Histogram

Amit Adam and Ehud Rivlin

Dept. of Computer Science

Technion - Israel Institute of Technology

Haifa 32000, Israel

{amita,ehudr}@cs.technion.ac.il

Ilan Shimshoni

Dept. of Management Information Systems

Haifa University

Haifa 31905, Israel

{ishimshoni}@mis.haifa.ac.il

Abstract

We present a novel algorithm (which we call “Frag-

Track”) for tracking an object in a video sequence. The

template object is represented by multiple image fragments

or patches. The patches are arbitrary and are not based on

an object model (in contrast with traditional use of model-

based parts e.g. limbs and torso in human tracking). Every

patch votes on the possible positions and scales of the ob-

ject in the current frame, by comparing its histogram with

the corresponding image patch histogram. We then mini-

mize a robust statistic in order to combine the vote maps of

the multiple patches.

A key tool enabling the application of our algorithm to

tracking is the integral histogram data structure [18]. Its

use allows to extract histograms of multiple rectangular re-

gions in the image in a very efﬁcient manner.

Our algorithm overcomes several difﬁculties which can-

not be handled by traditional histogram-based algorithms

[8, 6]. First, by robustly combining multiple patch votes, we

are able to handle partial occlusions or pose change. Sec-

ond, the geometric relations between the template patches

allow us to take into account the spatial distribution of the

pixel intensities - information which is lost in traditional

histogram-based algorithms. Third, as noted by [18], track-

ing large targets has the same computational cost as track-

ing small targets.

We present extensive experimental results on challenging

sequences, which demonstrate the robust tracking achieved

by our algorithm (even with the use of only gray-scale (non-

color) information).

1. Introduction

Tracking is an important subject in computer vision with

a wide range of applications - some of which are surveil-

lance, activity analysis, classiﬁcation and recognition from

motion and human-computer interfaces. The three main

categories into which most algorithms fall are feature-based

tracking (e.g. [3]), contour-based tracking (e.g. [15]) and

region-based tracking (e.g [13]). In the region-based cate-

gory, modeling of the region’s content by a histogram or by

other non-parametric descriptions (e.g. kernel-density esti-

mate) have become very popular in recent years. In particu-

lar, one of the most inﬂuential approaches is the mean-shift

approach [8, 6].

With the experience gained by using histograms and the

mean shift approach, some difﬁculties have been studied in

recent years. One issue is the local basin of convergence

that the mean shift algorithm has. Recently in [22] the au-

thors describe a method for converging to the optimum from

far-away starting points.

A second issue, inherent in the use of histograms, is the

loss of spatial information. This issue has been addressed

by several works. In [26] the authors introduce a new sim-

ilarity measure between the template and image regions,

which replaces the original Bhattacharyya metric. This

measure takes into account both the intensities and their

position in the window. The measure is further computed

efﬁciently by using the Fast Gauss Transform. In [12], the

spatial information is taken into account by using “oriented

kernels” - this approach is additionally shown to be useful

for wide baseline matching. Recently, [4] has addressed this

issue by adding the spatial mean and covariance of the pixel

positions who contribute to a given bin in the histogram -

naming this approach as “spatiograms”.

A third issue which is not speciﬁcally addressed by these

previous approaches is occlusions. The template model is

global in nature and hence cannot handle well partial occlu-

sions.

In this work we address the latter two issues (spatial in-

formation and occlusion) by using parts or fragments to rep-

resent the template. The ﬁrst issue is addressed by efﬁcient

exhaustive search which will be discussed later on. Given

a template to be tracked, we represent it by multiple his-

tograms of multiple rectangular sub regions (patches) of the

template. By measuring histogram similarity with patches

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