目标跟踪全面综述：方法分类与趋势探讨

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目标跟踪是计算机视觉领域中的关键问题，它涉及到在连续视频帧中持续追踪特定对象的位置和/或形状。本文《ObjectTracking: A Survey》由Alper Yilmaz、Omar Javed和Mubarak Shah共同撰写，他们分别来自俄亥俄州立大学、ObjectVideo, Inc.和佛罗里达中央大学。文章的主要目的是提供一个全面的概述，对当前最先进的目标跟踪方法进行分类，并探讨其内在的优缺点。首先，文章指出跟踪的对象可能面临多种挑战，包括快速的物体运动、对象和场景外观的变化、非刚体结构、遮挡以及摄像机移动。这些因素要求跟踪算法具备高度鲁棒性和适应性。为了简化问题并适用于特定应用，作者按照所使用的对象和运动表示方式进行方法分类。文章将跟踪方法分为几个主要类别： 1. **基于特征的方法**：这些方法依赖于图像特征，如颜色、纹理、形状或边缘信息，来识别和追踪对象。它们通常通过匹配当前帧中的特征点与先前帧中的特征来实现。优点是特征选择灵活，但可能受到光照、遮挡或复杂背景的影响。 2. **模板匹配**：通过对比当前帧与存储的模板图像，寻找最相似的部分作为目标位置。这种方法简单易用，但对于大规模运动或形态变化的对象效果较差。 3. **运动模型**：利用物理或统计模型（如光流、卡尔曼滤波）预测物体的运动，然后根据模型结果更新目标位置。这类方法能够处理一定程度的运动估计，但假设必须准确，否则可能导致跟踪失败。 4. **关联滤波器**：如粒子滤波和多目标跟踪算法，通过建立目标状态的概率分布，不断更新和融合观测信息。它们适合处理不确定性较高的情况，但计算复杂度较高。 5. **深度学习方法**：近年来，深度神经网络（如卷积神经网络）在目标跟踪中表现出色，通过端到端的学习能力，可以从大量数据中自动学习特征和目标表示，提高了追踪性能。然而，对数据质量和模型训练要求较高。 6. **联合模型**：结合多个方法的优势，如将视觉特征与运动模型相结合，形成更为复杂的系统。这能提高鲁棒性，但设计和优化更复杂。除了方法论，文章还讨论了重要的跟踪相关问题，比如选择合适的图像特征、处理多目标跟踪和跟踪初始化等。此外，文章也关注了新兴趋势，例如利用深度学习的在线和增量学习，以及实时性和计算效率的权衡。《ObjectTracking: A Survey》提供了对目标跟踪领域的深入理解，为研究人员和工程师提供了丰富的参考资源，帮助他们评估不同的方法，并根据实际需求选择最适合的技术。随着技术的发展，未来的目标跟踪方法将继续朝着更高的精度、鲁棒性和实时性迈进。

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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