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复杂场景下的运动目标跟踪算法研究
Alternative TitleResearch on Moving Object Tracking Algorithms under Complex Scenes
王思奎
Department光电信息技术研究室
Thesis Advisor刘云鹏
Keyword目标跟踪 相关滤波 背景约束 孪生网络 注意力机制
Pages67页
Degree Discipline控制工程
Degree Name硕士
2020-05-26
Degree Grantor中国科学院沈阳自动化研究所
Place of Conferral沈阳
Abstract运动目标跟踪技术涉及了图像处理、光电技术、模式识别和机器学习等多个领域的理论知识,在交通、安防、医疗、军事等领域发挥着重要的作用。对复杂场景下运动目标跟踪算法的改进有着重要的研究与应用价值。本文对近年目标跟踪算法的发展进行了综述,对相关跟踪算法的原理进行了解析。在此基础上,针对当前跟踪领域存在的复杂挑战,提出了两种鲁棒的在线目标跟踪算法,主要研究工作总结如下:(1)针对长期目标跟踪中因背景混叠和遮挡等因素导致的目标丢失问题,提出一种基于背景约束与卷积特征的长期目标跟踪方法。首先,对输入图像进行多特征融合并降维,增强目标特征判别性能的同时降低特征计算的复杂度。其次,在相关滤波器训练过程中引入背景约束,使得滤波器专注于目标响应,提升抗干扰能力。最后,通过设置记忆滤波器与峰值旁瓣比检测判断目标是否丢失。若丢失,引入卷积特征滤波器进行重检测,以实现目标的重捕。公开数据集实验证明,算法在混叠背景与遮挡场景下的表现优异,能够实现长期的稳定跟踪。(2)针对基于孪生网络结构的目标跟踪算法存在的特征利用效率低以及正负样本不均衡等问题,提出一种基于双重自注意力机制孪生网络的目标跟踪算法。首先,在孪生结构的骨干网络中引入通道自注意力与空间自注意力模块,对网络特征进行精炼。其次,使用反卷积的方式将不同层级的深度特征进行融合,充分利用卷积网络各阶段的表征能力。最后,引入在线难例挖掘的方式缓解正负样本不均衡问题,同时重点关注难样本的训练以提升网络在混叠背景下的判别能力。在背景模糊、快速移动、旋转、形变等多个复杂场景挑战下的对比实验证明了所提改进算法的有效性。
Other AbstractObject tracking technology covers theoretical knowledge in many fields such as image processing, photoelectric technology, pattern recognition, and machine learning, and plays an important role in transportation, security, medical, military and other fields. It has important research and application value to improve the moving target tracking algorithm in complex scenes. This thesis reviews the development of target tracking algorithms in recent years and analyzes the principles of related tracking algorithms. On this basis, two robust online target tracking algorithms are proposed in view of the complex challenges in the current tracking field. The work is summarized as follows: (1) A long-term object tracking method, which is based on background constraints and convolutional features, is proposed to solve the target loss problem caused by background aliasing and occlusion in long-term object tracking. Firstly, the feature of input image is fused and dimensionally reduced to enhance the performance of target feature discrimination and reduce the complexity of feature computation. Secondly, background constraints are introduced into the filter training process, which makes the filter more focused on the target response to improve the anti-jamming ability. Finally, by setting memory filter and the Peak to Sidelobe Ratio detection, the tracker can judge whether the target is missing or not. If the target is lost, a convolutional features filter is introduced to re-detect the target. The public data set experiment proves that the algorithm performs well under the background of aliasing with occlusion and achieves long-term stable tracking. (2) Aiming at the problems of low feature learning efficiency and imbalance of positive and negative samples in the target tracking algorithm based on the siamese network structure, a target tracking algorithm based on the dual self-attention mechanism siamese network is proposed. Firstly, the channel self-attention and spatial self-attention modules are introduced into the backbone network of the siamese structure to refine the network features. Secondly, the deconvolution strategy is used to concatenate the depth features of different levels, and make full use of the representation capabilities of each stage of the convolutional network. Finally, the method of online-hard-example-mining is introduced to alleviate the imbalance between positive and negative samples. At the same time, it focuses on the training of hard samples to improve the network's discriminating ability under the scenes of background clutter. The comparative experiments under the challenges of multiple complex scenes such as background clutter, fast motion, rotation, and deformation prove the effectiveness of the proposed algorithm.
Language中文
Contribution Rank1
Document Type学位论文
Identifierhttp://ir.sia.cn/handle/173321/27142
Collection光电信息技术研究室
Recommended Citation
GB/T 7714
王思奎. 复杂场景下的运动目标跟踪算法研究[D]. 沈阳. 中国科学院沈阳自动化研究所,2020.
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