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SiamMN: Siamese modulation network for visual object tracking

MULTIMEDIA TOOLS AND APPLICATIONS 2020年第43-44期79卷 32623-32641页

作者： Fu, Li-hua Ding, Yu Du, Yu-bin Zhang, Bo Wang, Lu-yuan Wang, Dan Beijing Univ Technol Fac Informat Technol Beijing 100124 Peoples R China

visual object tracking methods based on Siamese network are often difficult to distinguish objects with the same semantic or similar appearance as tracking target in tracking process due to the lack of discriminating strategies for the confusing objects. We propose a visual object tracking method based on Siamese modulation network. It takes the given bounding box in the target frame and the current frame as input, and fuses these multi-layer convolutional features to obtain more target appearance information of bounding box and the current frame. The feature modulator generates feature modulation vector based on the given bounding box to enhance visual appearance information of target instance in multi-layer feature of the current frame, so as to make target instance obtain higher score in response map of region proposal network, and thus realize target instance-specific tracking task. Experiments on two public benchmark datasets, OTB2015 and VOT2018, show that the proposed tracker has a competitive performance among other state-of-the art trackers.

关键词： visual object tracking Feature modulation Siamese network Region proposal network

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Dual Attention based Siamese Network for visual object tracking 2021

Dual Attention based Siamese Network for Visual Object Track...

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2021 International Conference on Frontiers of Electronics, Information and Computation Technologies, ICFEICT 2021

作者： Chen, Fei Wang, Xiaodong Yu, Wei National University of Defense Technology China

ISBN: (纸本)9781450390149

Video object tracking is a highly challenging problem, in which the initialization of the target object is given by the bounding box of first frame. The trackers based on deep Siamese network have achieved promising performance, while the robustness is still the key factor that affects the tracker's whole performance such as EAO in VOT datasets. In order to enhance the discriminability and robustness of the tracker, we introduce a dual-attentional Siamese network based tracker. In addition, we analyze the scenario that a target moves in a large scale and proposes an effective way to address this limitation. We perform extensive experiments on four public datasets. The experimental results illustrate that our novel tracker achives competitive tracking performance. © 2021 ACM.

关键词： Attentional Insert keyword text Siamese Network visual object tracking

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Learning Low-Rank and Sparse Discriminative Correlation Filters for Coarse-to-Fine visual object tracking

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IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY 2020年第10期30卷 3727-3739页

作者： Xu, Tianyang Feng, Zhen-Hua Wu, Xiao-Jun Kittler, Josef Jiangnan Univ Sch Internet Things Engn Wuxi 214081 Jiangsu Peoples R China Univ Surrey Ctr Vis Speech & Signal Proc Guildford GU2 7XH Surrey England

Discriminative correlation filter (DCF) has achieved advanced performance in visual object tracking with remarkable efficiency guaranteed by its implementation in the frequency domain. However, the effect of the structural relationship of DCF and object features has not been adequately explored in the context of the filter design. To remedy this deficiency, this paper proposes a Low-rank and Sparse DCF (LSDCF) that improves the relevance of features used by discriminative filters. To be more specific, we extend the classical DCF paradigm from ridge regression to lasso regression, and constrain the estimate to be of low-rank across frames, thus identifying and retaining the informative filters distributed on a low-dimensional manifold. To this end, specific temporal-spatial-channel configurations are adaptively learned to achieve enhanced discrimination and interpretability. In addition, we analyse the complementary characteristics between hand-crafted features and deep features, and propose a coarse-to-fine heuristic tracking strategy to further improve the performance of our LSDCF. Last, the augmented Lagrange multiplier optimisation method is used to achieve efficient optimisation. The experimental results obtained on a number of well-known benchmarking datasets, including OTB2013, OTB50, OTB100, TC128, UAV123, VOT2016 and VOT2018, demonstrate the effectiveness and robustness of the proposed method, delivering outstanding performance compared to the state-of-the-art trackers.

关键词： Target tracking visualization Correlation object tracking Neural networks Task analysis Feature extraction visual object tracking discriminative correlation filter lasso regression

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Attention shake siamese network with auxiliary relocation branch for visual object tracking

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NEUROCOMPUTING 2020年 400卷 53-72页

作者： Wang, Jun Liu, Weibin Xing, Weiwei Wang, Liqiang Zhang, Shunli Beijing Jiaotong Univ Inst Informat Sci Beijing 100044 Peoples R China Beijing Jiaotong Univ Sch Software Engn Beijing 100044 Peoples R China Univ Cent Florida Dept Comp Sci Orlando FL 32816 USA

Siamese network is highly regarded in the visual object tracking filed because of its unique advantages of pairwise input and pairwise training. It can measure the similarity between two image patches, which coincides with the principle of the matching-based tracking algorithm. In this paper, a variant Siamese network based tracker is proposed to introduce attention module into traditional Siamese network, and relocate the object with some auxiliary relocation methods, when the proposed tracker runs under an untrusted state. Firstly, a novel attention shake layer is proposed to replace the max pooling layer in Siamese network. This layer could introduce and train two different kinds of attention modules at the same time, which means the proposed attention shake layer could also help to improve the expression power of Siamese network without increasing the depth of the network. Secondly, an auxiliary relocation branch is proposed to assist in object relocation and tracking. According to the prior assumptions of visual object tracking, some weights are involved in the auxiliary relocation branch, such as structure similarity weight, motion similarity weight, motion smoothness weight and object saliency weight. Thirdly, a novel response map based switch function is proposed to monitor the tracking process and control the effect of auxiliary relocation branch. Furthermore, in order to discuss the effect of pooling layer in Siamese network, 9 pooling and attention architectures are proposed and discussed in this paper. Some empirical results are shown in the experiment part. Comparing with the state-of-the-art trackers, the proposed tracker could achieve comparable performance in multiple benchmarks. (C) 2020 Elsevier B.V. All rights reserved.

关键词： visual object tracking Siamese network Attention shake layer Auxiliary relocation branch Switch function

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SiamMFC: visual object tracking Based on Mainfold Full Convolution Siamese Network

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SENSORS 2021年第19期21卷 6388页

作者： Chen, Jia Wang, Fan Zhang, Yingjie Ai, Yibo Zhang, Weidong Univ Sci & Technol Beijing Natl Ctr Mat Serv Safety Beijing 100083 Peoples R China Southern Marine Sci & Engn Guangdong Lab Zhuhai 519082 Peoples R China

visual tracking task is divided into classification and regression tasks, and manifold features are introduced to improve the performance of the tracker. Although the previous anchor-based tracker has achieved superior tracking performance, the anchor-based tracker not only needs to set parameters manually but also ignores the influence of the geometric characteristics of the object on the tracker performance. In this paper, we propose a novel Siamese network framework with ResNet50 as the backbone, which is an anchor-free tracker based on manifold features. The network design is simple and easy to understand, which not only considers the influence of geometric features on the target tracking performance but also reduces the calculation of parameters and improves the target tracking performance. In the experiment, we compared our tracker with the most advanced public benchmarks and obtained a state-of-the-art performance.

关键词： visual object tracking geometric characteristics Siamese network manifold features

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Aberrance suppressed spatio-temporal correlation filters for visual object tracking

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PATTERN RECOGNITION 2021年 115卷 107922-107922页

作者： Elayaperumal, Dinesh Joo, Young Hoon Kunsan Natl Univ Sch IT Informat & Control Engn 588 Daehak Ro Gunsan Si 54150 Jeonbuk South Korea

The objective of the present study is to design a correlation filter-based tracking method for robust visual object tracking. In the literature, numerous tracking methods have been proposed based on discriminative correlation filter (DCF) and obtained impressive performance. However, existing algorithms still face difficulties such as partial occlusion, clutter background, uncertainties, boundary effects (especially when the target search area is small) and other challenging visual factors. Furthermore, during the target detection process, the sudden changes in objects caused by illumination variations and partial/full occlusion degrade the performance. To tackle the drawbacks mentioned earlier, we propose a tracking algorithm concerning the aberrance suppressed correlation filters with spatio-temporal information for visual tracking. Specifically, we introduce a spatial regularization term into the correlation filter to suppresses the boundary effects. Following that, a temporal regularization is adopted into the DCF-based framework to achieve a more robust appearance model and further enhance the tracking performance. In addition, we introduce an approach to suppress the aberrance in response maps caused by the sudden changes. Technically, our proposed method can be directly solved by using the alternating direction method of multipliers (ADMM) technique with a low computational cost. Finally, extensive experimental results on OTB2013, OTB2015, TempleColor128 and UAV123 datasets demonstrate that the proposed method performs favorably against state-of-the-art methods.& nbsp;(c) 2021 Elsevier Ltd. All rights reserved.

关键词： visual object tracking Correlation filter Spatio-temporal information Radical changes

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Learning Geometry Information of Target for visual object tracking with Siamese Networks

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SENSORS 2021年第23期21卷 7790页

作者： Chen, Hang Zhang, Weiguo Yan, Danghui Northwestern Polytech Univ Automat Coll Xian 710072 Peoples R China

Recently, Siamese architecture has been widely used in the field of visual tracking, and has achieved great success. Most Siamese network based trackers aggregate the target information of two branches by cross-correlation. However, since the location of the sampling points in the search feature area is pre-fixed in cross-correlation operation, these trackers suffer from either background noise influence or missing foreground information. Moreover, the cross-correlation between the template and the search area neglects the geometry information of the target. In this paper, we propose a Siamese deformable cross-correlation network to model the geometric structure of target and improve the performance of visual tracking. We propose to learn an offset field end-to-end in cross-correlation. With the guidance of the offset field, the sampling in the search image area can adapt to the deformation of the target, and realize the modeling of the geometric structure of the target. We further propose an online classification sub-network to model the variation of target appearance and enhance the robustness of the tracker. Extensive experiments are conducted on four challenging benchmarks, including OTB2015, VOT2018, VOT2019 and UAV123. The results demonstrate that our tracker achieves state-of-the-art performance.

关键词： visual object tracking deformable convolution deformable cross-correlation Siamese network

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IOU - SIAMTRACK: IOU GUIDED SIAMESE NETWORK FOR visual object tracking

IOU - SIAMTRACK: IOU GUIDED SIAMESE NETWORK FOR VISUAL OBJEC...

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IEEE International Conference on Image Processing (ICIP)

作者： Dasari, Mohana Murali Gorthi, Rama Krishna Sai Subrahmanyam Indian Inst Technol Dept Elect Engn Tirupati Andhra Pradesh India

ISBN: (纸本)9781728163956

Recently deep learning-based Siamese networks with region proposals for visual object tracking are becoming popular. These frameworks, while testing, perform extra computations on the output of a trained network to predict the bounding box (bbox). This process hinders end-to-end training of the above class of networks and hampers the precise estimation of the bbox in testing. In this paper, we propose a framework close to the Siamese class of networks, but guided by Intersection Over Union (IOU) to predict precise bbox directly in the image space rather than at the feature space. To maximise the IOU of predicted bbox with respect to ground truth, we introduce a new module and corresponding loss function in training the network. The proposed approach enables end-to-end training and testing under similar lines, circumventing the typical bottleneck of the existing Siamese trackers. When evaluated on VOT2018 and GOT-10k tracking benchmarks, the proposed approach outperformed the base approach by more than 10% in terms of average overlap and compares favourably to state-of-the-art methods.

关键词： visual object tracking deep learning for tracking Siamese trackers IOU region proposals

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Deep mutual learning for visual object tracking

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PATTERN RECOGNITION 2021年 112卷 107796-107796页

作者： Zhao, Haojie Yang, Gang Wang, Dong Lu, Huchuan Dalian Univ Technol Sch Informat & Commun Engn Dalian Peoples R China Northeastern Univ Coll Informat Sci & Engn Shenyang Peoples R China Xidian Univ Key Lab Integrated Serv Network Xian Peoples R China

Existing deep trackers use deep convolutional neural networks to extract powerful features or directly predict the position of the target. For most deep trackers, it is hard to improve their performance by replacing the original backbone with a more powerfully heavyweight network directly. In this paper, we propose a novel mutual-learning-based training methodology for visual object tracking. By re-training the backbone network with this novel methodology, we can improve the tracking performance simply and effectively. We demonstrate this novel training methodology with two mainstream tracking approaches: correlation-filter-based approach and tracking-by-detection-based approach. First, we reformulate a correlation-filter-based tracker as a fully convolutional network and design an end-to-end tracking framework. With this framework, we can enhance the backbone network in a mutual learning way. Second, we integrate our training methodology into a typical tracking-by-detection-based tracker, and then we improve the tracking performance with a simple offline training process. Extensive experiments on the OTB2013, OTB2015, VOT2017 and LaSOT benchmarks demonstrate that the tracking performance can be improved effectively by using the proposed mutual-learning-based training methodology. (c) 2020 Elsevier Ltd. All rights reserved.

关键词： visual object tracking Deep learning Mutual learning

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An Occlusion-Aware RGB-D visual object tracking Method Based on Siamese Network 15

An Occlusion-Aware RGB-D Visual Object Tracking Method Based...

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15th IEEE International Conference on Signal Processing (ICSP)

作者： Zhang, Wenli Yang, Kun Xin, Yitao Meng, Rui Beijing Univ Technol Fac Informat Technol Beijing Peoples R China

ISBN: (纸本)9781728144801

In recent years, Siamese trackers have been extensively studied because of their high accuracy and speed. However, when the target is occluded by other objects, the result will be greatly drifted, which affects the quality of the tracking results. This study based on the RGB-D data proposes an object tracking method integrating a target occlusion estimation module and a target location correction module, called Siamese-Occlusion-Correction (SiamOC). When a target is occluded, these modules can help the Siamese tracker correct the target location. In this paper, experiments demonstrate that the method which is the real-time tracker has achieved competitive results on the CDTB dataset.

关键词： Siamese network Occlusion-aware RGBD tracking visual object tracking

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