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correlation filter Tracker With Sample-Reliability Awareness and Self-Guided Update

IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY 2023年第1期33卷 118-131页

作者： Zhou, Lifang Li, Jiaqi Lei, Bangjun Li, Weisheng Leng, Jiaxu Chongqing Univ Posts & Telecommun Key Lab Image Cognit Chongqing 400065 Peoples R China Chongqing Univ Posts & Telecommun Coll Software Chongqing 400065 Peoples R China China Three Gorges Univ Hubei Key Lab Intelligent Vis Based Monitoring Hyd Yichang 443002 Peoples R China Chongqing Inst Brain & Intelligence Guangyang Bay Lab Chongqing 400064 Peoples R China Nanjing Univ Sci & Technol Jiangsu Key Lab Image & Video Understanding Social Nanjing 210094 Peoples R China

In visual tracking, unreliable samples always exist because of occlusion, illumination variation, motion blur, etc. Existing studies have effectively improved the performance of trackers by enhancing the quality of online samples. However, an underappreciated view is that not all samples are equally essential to model training. In this paper, we propose a Sample-Aware Adaptive Updating (SAAU) strategy which can actively adjust the update formula by sensing the reliability of samples. Specifically, the Sample-Reliability Awareness (SRA) module can quantify sample reliability by calculating three specific indicators, where the Residual Peak-to-correlation Energy (RPCE) is designed to cooperate with the other two introduced indicators to obtain credit scores on each sample. Besides, the Self-Guided Update (SGU) module provides a tracker with an unfixed learning rate that matches with the reliability label during updating, where our label annotator generates the label. Extensive experiments on several public benchmarks demonstrate the outstanding compatibility of SAAU and the superiority of our tracker (SAAU-CF) over state-of-the-art approaches.

关键词： Target tracking Reliability Training Tracking correlation Visualization Optimization Visual object tracking correlation filter model updating sample reliability

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correlation filter tracking algorithm based on spatial-temporal regularization and context awareness

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APPLIED INTELLIGENCE 2022年第15期52卷 17772-17783页

作者： Wu, Xuedong Xu, Jie Zhu, Zhiyu Wang, Yaonan Zhang, Qiang Tang, Siming Liang, Mengquan Cao, Baiheng Jiangsu Univ Sci & Technol Sch Elect & Informat Zhenjiang 212100 Jiangsu Peoples R China Hunan Univ Coll Elect & Informat Engn Changsha 410082 Peoples R China

The tracker based on correlation filters can achieve effective positioning at a relatively fast speed, resulting from its operation in the frequency domain. As a result, it is commonly employed in the field of object tracking. However, this characteristic introduces boundary effect and affects the tracking performance in some scenes. In this work, a correlation filter tracking algorithm with spatial-temporal regularization and context awareness (STCACF) is proposed: (1) the spatial-temporal information and context awareness is added to the training process to mitigate the boundary effect and enhance the overall tracking performance;(2) the tracker model adopts the iterative method of alternating direction method of multipliers (ADMM), so that each subproblem can be solved in a closed-loop solution, which can realize real-time tracking;(3) the spatial regularization is employed to reduce the influence of filter degradation. Experiments on the OTB-2013, the OTB-2015 and the TC-128 benchmark datasets demonstrate that the suggested STCACF is capable of significantly improving the tracking performance compared with state-of-the-art trackers. The STCACF tracker runs at a frame rate of approximately 22 frames per second (FPS) on a single central processing unit (CPU).

关键词： correlation filter Boundary effect Spatial-temporal regularization Context awareness

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Siamese Deep Q-Learning Based Online correlation filter Adaptation for Visual Object Tracking in Complex Scenarios

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CIRCUITS SYSTEMS AND SIGNAL PROCESSING 2025年 1-44页

作者： Shajeena, J. Shiny, R. M. Palas, P. Bini Vespa, M. Mary Stanley, Berakhah F. Kumar, R. Jeen Retna SRM Inst Sci & Technol Dept Comp Sci & Engn Tiruchirappalli Campus Tiruchirapalli Tamil Nadu India St Josephs Coll Engn Dept Comp Sci & Engn Chennai Tamil Nadu India Easwari Engn Coll Dept Elect & Commun Engn Chennai Tamil Nadu India Vel Tech Rangarajan Dr Sagunthala R&D Inst Sci & T Dept Comp Sci & Engn Chennai Tamil Nadu India Vel Tech Rangarajan Dr Sagunthala R&D Inst Sci & T Dept Elect & Commun Engn Chennai Tamil Nadu India

Visual Object Tracking (VOT) is essential for real-time monitoring and analysis across various applications, but Siamese tracker networks face significant challenges, including overfitting, occlusion handling, fast motion, complex backgrounds, and adaptation to appearance changes. To address these challenges, we propose a novel tracker model named Siamese Deep Q-learning based online correlation filter Adaptation (SiamDQCFA). SiamDQCFA uses two identical subnetworks to capture evolving appearance changes and semantic similarities between the target object and search region patches, enabling effective feature extraction and adaptive parameter adjustment. This architecture extracts high-level feature representations that predict the target's location in subsequent frames, improving accuracy over time. The model evaluates target presence in the search region using cosine similarity as a measure. Furthermore, SiamDQCFA incorporates an online correlation filter adaptation mechanism that dynamically adjusts its parameters to enhance target localization, improving the robustness of the tracker to various environmental challenges. Through extensive experimental evaluations on benchmark datasets such as OTB100, TrackingNet, VOT2019, and LaSOT, SiamDQCFA demonstrates superior performance, outperforming state-of-the-art tracking methods across multiple metrics. These results underscore the model's effectiveness in addressing the key challenges of VOT, which makes it a robust solution for real-time tracking in challenging conditions.

关键词： Visual object tracking (VOT) Siamese network Reinforcement learning (RL) correlation filter Adaptability Benchmark datasets

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SOCF: A correlation filter for real-time UAV tracking based on spatial disturbance suppression and object saliency-aware

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EXPERT SYSTEMS WITH APPLICATIONS 2024年第PartE期238卷

作者： Ma, Sugang Zhao, Bo Hou, Zhiqiang Yu, Wangsheng Pu, Lei Yang, Xiaobao Xian Univ Posts & Telecommun Sch Comp Sci & Technol Xian 710121 Peoples R China Air Force Engn Univ Sch Informat & Nav Xian 710077 Peoples R China Rocket Force Univ Engn Sch Operat Support Xian 710025 Peoples R China Northwestern Polytech Univ Sch Comp Sci & Engn Xian 710072 Peoples R China

The discriminative correlation filter (DCF) is commonly used in aerial object tracking due to its high tracking accuracy and computing speed. However, when similar object disturbances emerge in the background, the response map will generate sub-peaks, which may eventually lead to tracking failure. Meanwhile, the lack of attention to the tracked object can also cause tracking performance degradation. To these concerns, this paper proposes a novel correlation filter algorithm for real-time aerial tracking based on spatial disturbance suppression and object saliency-aware, i.e., SOCF. Firstly, this paper designs a novel spatial disturbance suppression strategy. Using the temporal information in the historical response maps, we construct a context response map, deviating it from the current response map to detect disturbance information in the background. Then, construct a spatial interference map, divide it into n x n non-overlapping regions, and suppress the negative samples in the disturbance region within the main regression. Furthermore, an object saliency-aware strategy is proposed, using a saliency detection algorithm to calculate the object-aware mask and multiplying it with the detection filter to obtain the object-aware filter. By constructing the object-aware regularization in the training phase, the trained detection filter focuses more on the object itself and can effectively separate the object from the background. Extensive experiments on four widely used unmanned aerial vehicle (UAV) datasets demonstrate that the proposed SOCF tracker achieves high tracking performance. Meanwhile, our tracker can maintain real-time aerial tracking at 48 FPS on a single CPU.

关键词： Object tracking correlation filter Spatial disturbance suppression Saliency-aware

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Adaptive target response-based spatio-temporal regularized correlation filter for UAV-based object tracking

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SIGNAL IMAGE AND VIDEO PROCESSING 2024年第5期18卷 4763-4778页

作者： Bhunia, Himadri Sekhar Deb, Alok Kanti Mukherjee, Jayanta IIT Kharagpur Adv Technol Dev Ctr Kharagpur 721302 West Bengal India IIT Kharagpur Elect Engn Kharagpur 721302 West Bengal India IIT Kharagpur Comp Sci Engn Kharagpur 721302 West Bengal India

Unmanned Aerial Vehicles (UAVs)-based visual object tracking has drawn significant attention recently and is extensively used in military applications, aviation, security, and agriculture, to name a few. Discriminative correlation filter (DCF)-based trackers have gained significant recognition in UAV-based visual tracking due to their efficiency and tracking speed. However, the training samples generated by cyclic shifts in the correlation filter are not real samples. In many cases, these approximate samples differ significantly from actual samples. Due to this, the traditional correlation filter-based trackers have inherent boundary effects and filter degradation issues. The traditional assumption of a single-centred Gaussian target response may not be reliable in challenging situations such as fast motion or occlusion. Such unreliable training samples lead to tracker drift. This paper proposes a novel Adaptive Target Spatio-Temporal Regularized correlation filter (ATCF) tracker to rectify these issues. A simple yet effective energy function is developed by combining the adaptive spatio-temporal regularized correlation filter and the adaptive target response. The closed-form solution is obtained using the Alternating Direction Multiplier Method (ADMM). The target response and spatio-temporal regularization parameters are learned online. Besides, a novel detection-based re-tracking strategy is introduced to improve long-term tracking performance. The proposed tracker's performance on four benchmark datasets, i.e. DTB70, UAV123@10fps, UAV20L, and UAVDT benchmarks, has proved its superiority over various state-of-the-art trackers in terms of accuracy and robustness while running in real time in a CPU environment.

关键词： correlation filter Object tracking Spatio-temporal regularization Adaptive target UAV tracking

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Motion-aware object tracking for aerial images with deep features and discriminative correlation filter

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MULTIMEDIA TOOLS AND APPLICATIONS 2024年第30期83卷 75369页

作者： Delibasoglu, Ibrahim Sakarya Univ Fac Comp & Informat Sci Dept Software Engn TR-54050 Sakarya Turkiye

Object tracking is a challenging task which is required for different problems such as surveillance, traffic analysis and human-computer interaction. The problem of tracking an object can be considered in different categories such as single object tracking, multiple object tracking, short-term tracking, long-term tracking, tracking by detection and detection-free tracking. This study focuses on detection-free tracking for ground targets on aerial images. The studies in the literature show that correlation filter and deep learning based object trackers perform well recently. This paper proposes a new correlation filter-based tracker containing a strategy for re-detection issue. We improve the performance of correlation filter-based tracker by adding a lightweight re-detection ability to the correlation filter tracker in case of a long occlusion or complete loss of target. We use deep features to train Discriminative correlation filter(DCF) by integrating sub-networks from pre-trained ResNet and SAND models. The experimental results on the popular UAV123L dataset show that the proposed method(MADCF) improves the performance of DCF tracker and have a reasonable performance for long-term tracking problem. Moreover, we prepare a new tracking dataset (PESMOD tracking) consisting of UAV images, and we evaluate the proposed method and state-of-the-art method in this dataset. We observed that the proposed method performs much better in ground target tracking from VIVID and PESMOD aerial datasets. The proposed MADCF tracker performs better for small targets tracked by UAVs compared to the deep learning-based trackers. The source code and prepared dataset are available at http://***/mribrahim/MADCF

关键词： Object tracking Visual tracking correlation filter DCF Long-term tracking

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Spatial Reliability Enhanced correlation filter: An Efficient Approach for Real-Time UAV Tracking

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IEEE TRANSACTIONS ON MULTIMEDIA 2024年 26卷 4123-4137页

作者： Fu, Changhong Jin, Jin Ding, Fangqiang Li, Yiming Lu, Geng Tongji Univ Sch Mech Engn Shanghai 201804 Peoples R China Swiss Fed Inst Technol Dept Management Technol & Econ Zurich Switzerland Univ Edinburgh Sch Informat Edinburgh EH8 9YL Scotland NYU Tandon Sch Engn New York NY 11201 USA Tsinghua Univ Dept Automat Beijing 100084 Peoples R China

Traditional discriminative correlation filter (DCF) has received great popularity due to its high computational efficiency. However, the lightweight framework of DCF cannot promise robust performance when the tracker faces appearance variations within the background. These unpredictable appearance variations always distract the filter. Most existing DCF-based trackers either utilize deep convolutional features or incorporate additional constraints to elevate tracking robustness. Despite some improvements, both of them hamper the tracking speed and can only roughly alleviate the distractions of appearance variations. In this paper, a novel spatial reliability enhanced learning strategy is proposed to handle the problems aforementioned. By monitoring the variation of response produced in detection phase, a dynamic reliability map is generated to indicate the reliability of each background subregion. Then, label adjustment is conducted to repress the distractions of these unreliable areas. Compared with the conventional way of constraint where a new term is always added to realize the desired goal, label adjustment is simultaneously more efficient and effective. Moreover, to promise the accuracy and dependability of the reliability map, an adaptively updated response pool recording reliable historical response values is proposed. Extensive and exhaustive experiments on three challenging unmanned aerial vehicle (UAV) benchmarks, i.e., UAV123@10fps, DTB70 and UAVDT, which totally include 243 video sequences, validate the superiority of the proposed method against other state-of-the-art trackers and exhibit a remarkable generality in a variety of scenarios. Meanwhile, the tracking speed of 65.2FPS on a cheap CPU makes it suitable for real-time UAV applications.

关键词： Target tracking Reliability correlation Real-time systems Unmanned aerial vehicles Training Robustness UAV tracking spatial reliability map response pool label adjustment correlation filter appearance variation

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Background-Aware correlation filter for Object Tracking with Deep CNN Features

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ENGINEERING LETTERS 2024年第7期32卷 1353-1363页

作者： Chen, Kaiwei Wang, Lingzhi Wu, Huangyu Wu, Changhui Liao, Yuan Chen, Yingpin Wang, Hui Yan, Jingwen Lin, Jialing He, Jiale Minnan Normal Univ Zhangzhou 363000 Peoples R China Xiamen City Univ Xiamen 361000 Peoples R China Minnan Normal Univ Sch Phys & Informat Engn Key Lab Light Field Manipulat & Syst Integrat Appl Zhangzhou 363000 Peoples R China Shantou Univ Shantou 515000 Peoples R China

correlation filter tracking algorithms have garnered significant attention due to their efficiency and outstanding tracking performance. However, these methods face several limitations. Firstly, they rely on periodic boundary conditions, leading to boundary effects. Secondly, most traditional methods only extract hand-crafted features from the image. Nevertheless, these features are insufficient to discriminate in complex scenes. Thirdly, they assume that the maximum position of the correlation response represents the object without further evaluating its reliability. These limitations make it very easy to lose the object in occluded scenes. A background-aware correlation filter algorithm based on an anti-occlusion mechanism and deep features is proposed to solve the above limitations. Primarily, the video frames to be processed are cyclically shifted to crop the cyclically shifted samples in a small window. This operation significantly reduces boundary effects while obtaining background samples from the real world. Then, deep features are extracted through deep neural networks. Subsequently, we propose a background perception tracking framework that synchronously estimates position and scale based on these features. This framework aims to determine the optimal candidate sample position and scale. Finally, an anti-occlusion mechanism is constructed to evaluate the optimal candidate samples obtained in each frame further. This mechanism fully exploits the diversity of objects and effectively solves the tracking drift and failure issues caused by occlusion, fast motion, and so on. Extensive experiments are conducted on the object tracking benchmark (OTB) dataset and compared with industry-leading tracking algorithms to validate the effectiveness of the proposed method. The results show that the method has robust tracking performance.

关键词： object tracking correlation filter background-aware anti-occlusion mechanism deep feature

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Robust cascaded-parallel visual tracking using collaborative color and correlation filter models

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MULTIMEDIA TOOLS AND APPLICATIONS 2023年第1期83卷 33-59页

作者： Hao, Zhaohui Liu, Guixi Zhang, Haoyang Wang, Fei Xidian Univ Sch Mechano Elect Engn Xian 710071 Shaanxi Peoples R China Henan Inst Sci & Technol Sch Informat Engn Xinxiang 453003 Henan Peoples R China

In recent years, the multi-expert collaborative tracking strategy has been introduced into visual tracking tasks and achieves impressive performance. Different from most existing multi-expert trackers that linearly fuse multiple tracking models, we propose a novel cascaded-parallel tracking algorithm (CPT) via adaptively selecting the suitable expert among multiple tracking models. And the CPT consists of cascaded and parallel tracking components. In the cascaded tracking component, we hierarchically implement two effective correlation filter models to coarse-to-fine locate the target. And in the parallel tracking component, a color tracking model is applied to locate the target to compensate for the demerit of the correlation filter models. With the proposed adaptive expert selection mechanism, the most reliable expert (i.e. tracking model) is selected for tracking in each frame. Extensive experimental results on OTB2013, OTB2015 and TempleColor128 datasets demonstrate that our proposed algorithm performs favorably against some state-of-the-art algorithms.

关键词： correlation filter Cascaded-parallel tracking Target likelihood probability Reliable expert selection analysis

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A robust spatial-temporal correlation filter tracker for efficient UAV visual tracking

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APPLIED INTELLIGENCE 2023年第4期53卷 4415-4430页

作者： Chen, Lin Liu, Yungang Shandong Univ Sch Control Sci & Engn Jinan 250061 Peoples R China

correlation filters (CFs) have exhibited remarkable performance in visual target tracking, especially in aerial tracking of unmanned aerial vehicles (UAVs). Most existing CF-based trackers focus on how to effectively settle the unwanted boundary effect problem, while ignoring the different contributions of the discriminative features, which would lead to suboptimal performance in tracking. In this work, a robust spatial-temporal correlation filter, i.e., the temporal regularized background-aware correlation filter (TRBCF), is proposed. In detail, by extracting real background patches as negative samples and introducing a temporal regularization term, TRBCF improves the discriminability between the target and background in the spatial domain, and achieves continuous tracking in temporal sequences. Moreover, in order to selectively highlight the informative features and effectively represent the target, a novel multi-feature fusion mechanism based on the channel-wise response maps is proposed. Extensive experiments are conducted to evaluate the effectiveness of the proposed TRBCF on three classical UAV datasets (UAV123@10fps, DTB70, and UAVDT), and TRBCF performs favorably compared with the state-of-the-art trackers, with a real-time speed (41.38 fps) on a single CPU.

关键词： UAV visual tracking correlation filter Spatial-temporal Multi-feature fusion

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