目标跟踪技术：现状与趋势

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"Object Tracking: A Survey" 这篇文章是对目标追踪技术的全面综述，旨在梳理当前最先进的跟踪方法，对其进行分类，并识别新的趋势。目标追踪是一个复杂的问题，常常面临诸多挑战，如物体突然运动、物体与场景外观模式的变化、非刚性物体结构、物体间及物体与场景间的遮挡，以及相机的运动等。追踪通常是在需要在每一帧中确定对象位置和/或形状的更高层次应用中进行的。为此，特定应用中通常会做出一些假设来约束追踪问题。在这篇调查中，作者根据对象表示和运动表示对追踪方法进行了分类，并详细介绍了每个类别中的代表性方法，分析了它们的优点和缺点。此外，文章还讨论了与追踪相关的关键问题，包括选择合适的图像特征、选择运动模型，以及如何检测目标。文章深入探讨了目标追踪的各个重要方面，例如： 1. **对象表示**：追踪方法的不同在于它们如何表示和描述跟踪的目标。这可能涉及到颜色、纹理、形状、运动特征或其他高级属性。每种表示都有其优势和局限性，适用于不同类型的追踪场景。 2. **运动模型**：选择正确的运动模型对于跟踪至关重要。这些模型可以基于统计、物理或学习方法，用于预测目标在下一帧的位置。选择适合当前应用场景的运动模型能提高追踪的鲁棒性和准确性。 3. **图像特征**：有效的图像特征是成功追踪的关键。从简单的颜色直方图到复杂的兴趣点描述符，各种特征被用于区分目标并使其在不同的光照、角度和遮挡条件下保持可识别。 4. **遮挡处理**：面对物体间或物体与背景的遮挡，追踪算法需要有能力恢复丢失的目标，或者在遮挡后重新初始化追踪。 5. **相机运动**：当相机移动时，追踪算法必须能够适应视图的变化，保持对目标的稳定追踪。 6. **评估与比较**：文章可能会讨论如何评估和比较不同追踪方法的性能，通常使用如清晰度、精度和连贯性等指标。 7. **未来趋势**：作者还会展望未来的追踪技术，可能包括深度学习、多模态信息融合、自适应追踪策略等方面的进展。这篇综述对于理解和研究目标追踪领域的最新发展具有很高的参考价值，无论是对初学者还是经验丰富的研究人员，都能从中获得深入的洞察和启发。

8 A. Yilmaz et al.

Fig. 2. Interest points detected by applying (a) the Harris, (b) the KLT, and (c) SIFT operators.

The Harris detector computes the ﬁrst order image derivatives, (I

, I

), in x and y di-

rections to highlight the directional intensity variations, then a second moment matrix,

which encodes this variation, is evaluated for each pixel in a small neighborhood:

M =







. (1)

An interest point is identiﬁed using the determinant and the trace of M which mea-

sures the variation in a local neighborhood R =det(M)−k ·tr(M )

, where k is constant.

The interest points are marked by thresholding R after applying nonmaxima suppres-

sion (see Figure 2(a) for results). The source code for Harris detector is available at

HarrisSrc. The same moment matrix M given in Equation (1) is used in the interest

point detection step of the KLT tracking method. Interest point conﬁdence, R, is com-

puted using the minimum eigenvalue of M , λ

min

. Interest point candidates are selected

by thresholding R. Among the candidate points, KLT eliminates the candidates that

are spatially close to each other (Figure 2(b)). Implementation of the KLT detector is

available at KLTSrc.

Quantitatively both Harris and KLT emphasize the intensity variations using very

similar measures. For instance, R in Harris is related to the characteristic polynomial

used for ﬁnding the eigenvalues of M : λ

+det(M ) −λ ·tr(M ) =0, while KLT computes

the eigenvalues directly. In practice, both of these methods ﬁnd almost the same interest

points. The only difference is the additional KLT criterion that enforces a predeﬁned

spatial distance between detected interest points.

In theory, the M matrix is invariant to both rotation and translation. However, it

is not invariant to afﬁne or projective transformations. In order to introduce robust

detection of interest points under different transformations, Lowe [2004] introduced

the SIFT (Scale Invariant Feature Transform) method which is composed of four steps.

First, a scale space is constructed by convolving the image with Gaussian ﬁlters at

different scales. Convolved images are used to generate difference-of-Gaussians (DoG)

images. Candidate interest points are then selected from the minima and maxima of

the DoG images across scales. The next step updates the location of each candidate by

interpolating the color values using neighboring pixels. In the third step, low contrast

candidates as well as the candidates along the edges are eliminated. Finally, remaining

interest points are assigned orientations based on the peaks in the histograms of

gradient directions in a small neighborhood around a candidate point. SIFT detector

generates a greater number of interest points compared to other interest point detec-

tors. This is due to the fact that the interest points at different scales and different

resolutions (pyramid) are accumulated. Empirically, it has been shown in Mikolajczyk

and Schmid [2003] that SIFT outperforms most point detectors and is more resilient

to image deformations. Implementation of the SIFT detector is available at SIFTSrc.

ACM Computing Surveys, Vol. 38, No. 4, Article 13, Publication date: December 2006.

Object Tracking: A Survey 9

Fig. 3. Mixture of Gaussian modeling for background subtraction. (a) Image from a sequence

in which a person is walking across the scene. (b) The mean of the highest-weighted Gaussians

at each pixels position. These means represent the most temporally persistent per-pixel color

and hence should represent the stationary background. (c) The means of the Gaussian with

the second-highest weight; these means represent colors that are observed less frequently. (d)

Background subtraction result. The foreground consists of the pixels in the current frame that

matched a low-weighted Gaussian.

4.2. Background Subtraction

Object detection can be achieved by building a representation of the scene called the

background model and then ﬁnding deviations from the model for each incoming frame.

Any signiﬁcant change in an image region from the background model signiﬁes a moving

object. The pixels constituting the regions undergoing change are marked for further

processing. Usually, a connected component algorithm is applied to obtain connected

regions corresponding to the objects. This process is referred to as the background

subtraction.

Frame differencing of temporally adjacent frames has been well studied since the

late 70s [Jain and Nagel 1979]. However, background subtraction became popular fol-

lowing the work of Wren et al. [1997]. In order to learn gradual changes in time, Wren

et al. propose modeling the color of each pixel, I(x, y), of a stationary background

with a single 3D (Y, U, and V color space) Gaussian, I (x, y) ∼ N (μ(x, y), (x, y)). The

model parameters, the mean μ(x, y ) and the covariance (x, y), are learned from the

color observations in several consecutive frames. Once the background model is de-

rived, for every pixel (x, y) in the input frame, the likelihood of its color coming from

N(μ(x, y ), (x, y)) is computed, and the pixels that deviate from the background model

are labeled as the foreground pixels. However, a single Gaussian is not a good model for

outdoor scenes [Gao et al. 2000] since multiple colors can be observed at a certain loca-

tion due to repetitive object motion, shadows, or reﬂectance. A substantial improvement

in background modeling is achieved by using multimodal statistical models to describe

per-pixel background color. For instance, Stauffer and Grimson [2000] use a mixture

of Gaussians to model the pixel color. In this method, a pixel in the current frame is

checked against the background model by comparing it with every Gaussian in the

model until a matching Gaussian is found. If a match is found, the mean and vari-

ance of the matched Gaussian is updated, otherwise a new Gaussian with the mean

equal to the current pixel color and some initial variance is introduced into the mix-

ture. Each pixel is classiﬁed based on whether the matched distribution represents the

background process. Moving regions, which are detected using this approach, along

with the background models are shown in Figure 3.

Another approach is to incorporate region-based (spatial) scene information instead

of only using color-based information. Elgammal and Davis [2000] use nonparamet-

ric kernel density estimation to model the per-pixel background. During the sub-

traction process, the current pixel is matched not only to the corresponding pixel in

the background model, but also to the nearby pixel locations. Thus, this method can

handle camera jitter or small movements in the background. Li and Leung [2002]

fuse the texture and color features to perform background subtraction over blocks of

ACM Computing Surveys, Vol. 38, No. 4, Article 13, Publication date: December 2006.

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目标跟踪技术：现状与趋势

翻译：Object Tracking: A Survey，物体跟踪：一种方法

CSUR 2012-ACM Computing Surveys 2012

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