MC-HOG特征与跟踪策略：结合显着性建模

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"MC-HOG相关跟踪与显着性建议，这篇研究论文探讨了在线视觉跟踪中的关键问题，包括有效的特征设计和模型漂移处理。MC-HOG是一种利用多个颜色通道或空间的丰富梯度信息构建的丰富特征描述符，被嵌入到相关跟踪框架中以估计目标状态。同时，论文提出了一种显着性提案方法来应对模型漂移问题，旨在提高跟踪性能和鲁棒性。" 在计算机视觉领域，视觉跟踪是一项基础且重要的任务，用于在连续的视频帧中定位和跟踪特定目标。本文提出的MC-HOG（Multi-channel Color Histogram of Oriented Gradients）是对传统的HOG（Histogram of Oriented Gradients）特征的扩展。HOG特征因其对物体形状和纹理的敏感性而广泛应用于目标检测和跟踪，但通常仅基于单个色彩空间。MC-HOG引入了多通道的概念，考虑了不同颜色空间的梯度信息，从而提高了特征的描述能力，增强了目标识别的准确性。将MC-HOG特征集成到相关跟踪框架中，能够更好地捕捉目标的运动模式和外观变化。相关滤波器是一种高效的相关跟踪方法，通过学习目标和背景的差异来更新跟踪模型。MC-HOG与这种框架的结合，使得跟踪算法可以更准确地估计目标的状态，减少了由于目标外观变化或环境干扰导致的跟踪漂移。然而，跟踪过程中常见的模型漂移问题仍然存在，即随着时间的推移，跟踪模型可能逐渐偏离真实目标。为了应对这个问题，论文提出了显着性提案策略。显着性检测通常用于找出图像中最吸引人或最不寻常的部分，这里它被用来检测潜在的目标区域，辅助校正模型漂移。通过结合显着性信息，跟踪算法可以更加敏锐地检测到目标的变化，及时更新模型，从而提高跟踪的稳定性和持久性。 MC-HOG相关跟踪与显着性建议为解决视觉跟踪中的关键挑战提供了新的思路。通过结合多通道梯度信息和显着性检测，该方法有望提升跟踪算法的性能，尤其在面对复杂背景、目标遮挡和光照变化等挑战时。这项工作对于视觉跟踪领域的理论研究和实际应用都具有重要意义，为未来的研究提供了有价值的参考。

MC-HOG Correlation Tracking with Saliency Proposal

Guibo Zhu

†

, Jinqiao Wang

†

,YiWu

‡

, Xiaoyu Zhang

, and Hanqing Lu

†

National Laboratory of Pattern Recognition,

Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China

‡

B-DAT & CICAEET, School of Information & Control,

Nanjing University of Information Science and Technology, Nanjing, 210044, Jiangsu, China

Institute of Information Engineering, Chinese Academy of Sciences, Beijing, 100093, China

{gbzhu, jqwang, luhq}@nlpr.ia.ac.cn

ywu.china@yahoo.com, zhangxiaoyu@iie.ac.cn

Abstract

Designing effective feature and handling the model drift

problem are two important aspects for online visual track-

ing. For feature representation, gradient and color features

are most widely used, but how to effectively combine them

for visual tracking is still an open problem. In this paper, we

propose a rich feature descriptor, MC-HOG, by leveraging

rich gradient information across multiple color channels or

spaces. Then MC-HOG features are embedded into the cor-

relation tracking framework to estimate the state of the target.

For handling the model drift problem caused by occlusion or

distracter, we propose saliency proposals as prior information

to provide candidates and reduce background interference. In

addition to saliency proposals, a ranking strategy is proposed

to determine the importance of these proposals by exploiting

the learnt appearance ﬁlter, historical preserved object sam-

ples and the distracting proposals. In this way, the proposed

approach could effectively explore the color-gradient char-

acteristics and alleviate the model drift problem. Extensive

evaluations performed on the benchmark dataset show the su-

periority of the proposed method.

1 Introduction

Visual tracking, which is to estimate object state in an im-

age sequence, is one of the core problems in computer vi-

sion. It has many applications, such as surveillance, action

recognition and autonomous robots/car (Yilmaz, Javed, and

Shah 2006; Wang et al. 2014). One robust visual tracking ap-

proach in real-world scenarios should cope with challenges

as much as possible, such as occlusions, background clutter

and shape deformation.

Feature representation is critical for improving the per-

formance in object detection (Doll

ar et al. 2009), tracking

(Henriques et al. 2015), age estimation (Li et al. 2012) and

image ranking (Li et al. 2014). Gradient and color features

are the most widely used ones. To be speciﬁc, Histogram

of Oriented Gradient (HOG) (Dalal and Triggs 2005) fea-

tures are good at describing abundant gradient information

while color features like color histograms often capture rich

color characteristics. For example, integral channel features

proposed by (Doll

ar et al. 2009) and its expansions (Doll

 2016, Association for the Advancement of Artiﬁcial

et al. 2014) have achieved good results in object detection

by concatenating gradient and color features directly. Tang

et al. explored the complementary between color histogram

and HOG features in a co-training framework for tracking

(Tang et al. 2007). Although color and gradient features are

widely used in vision based applications, there is no detailed

analysis on the gradient properties for the same target in dif-

ferent color spaces. Therefore, it is interesting to exploit this

kind of gradient properties for effective feature representa-

tion. Inspired by color naming (CN) which transforms RGB

color space into an 11-D probabilistic space (Van De Wei-

jer et al. 2009), the image pixels are projected into multi-

ple color channels to extract gradient features for construct-

ing a new feature descriptor: HOG extracted across Multi-

ple Color channels (MC-HOG). It is a more natural fusion

strategy than direct concatenation which needs to consider

the feature normalization problem across different feature

spaces.

Associated with object tracking, model drift means that

the object appearance model gradually drifts away from the

object due to its accumulated errors caused by online up-

date (Matthews, Ishikawa, and Baker 2004). There are many

strategies to alleviate the drift problem, e.g. semi-supervised

learning (Grabner, Leistner, and Bischof 2008), ensemble-

based learning (Tang et al. 2007; Kwon and Lee 2010), long-

term detector (Kalal, Mikolajczyk, and Matas 2012) and part

context learning (Zhu et al. 2015). In essence, they either ex-

plored the supervised information of the training samples or

the search strategy. However, the reliability of training sam-

ples collected online is difﬁcult to guarantee. To provide rel-

atively less candidate regions and suppress the background

interference, in this paper we introduce saliency proposals

as prior information from visual saliency, which has been

studied by many researchers (Itti, Koch, and Niebur 1998;

Harel, Koch, and Perona 2006) and owns good character-

istics for automatic target initialization and scale estima-

tion (Seo and Milanfar 2010; Mahadevan and Vasconcelos

2013). The saliency map is taken as the prior information

to obtain candidate proposals which are more efﬁcient than

exhaustive search based on sliding windows. In addition to

saliency proposals, a ranking strategy is proposed to deter-

mine the importance of these proposals and estimate the

Proceedings of the Thirtieth AAAI Conference on Artificial Intelligence (AAAI-16)

3690

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