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Single Target visual object tracking 6

6th International Conference on Advanced Computing and Communication Technologies for High Performance Applications (ACCTHPA)

作者： Kumar, Shiv Singh, Sandeep Kumar IIT ROPAR Dept Elect Engn Ropar Punjab India NIT HAMIRPUR Dept Elect & Commun Engn Hamirpur Himachal Prades India

ISBN: (纸本)9798350398441

In the present world, the evolution of technology takes place in terms of developing new things to solve some real-world problems. Almost all fields are creative in the sense of creating new things. Computer vision is one of the fields that give the technology that can help with different real-time problems. This field offers some useful applications over the last few decades. Different technologies like Autonomous cars, Traffic flow analysis, X-ray analysis, etc. make this field interesting for researchers. object detection is one of the applications of Computer vision. object detection and tracking enable the machine to detect the object and track the object through the entire scene. In this paper, a supervised learning approach for object detection is presented which consists of a detector, a Neural network, Images, and Groundtruths. The presented model successfully tracks the object and the dataset used for the performance evaluation is Online object tracking Benchmark (OOTB), Unmanned Aerial Vehicle (UAV), and visual object tracking Benchmark (VOT) dataset.

关键词： Neural network visual object tracking Bounding boxes Attribute analysis

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Learning spatio-temporal discriminative model for affine subspace based visual object tracking

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visual Intelligence 2023年第1期1卷 1-13页

作者： Xu, Tianyang Zhu, Xue-Feng Wu, Xiao-Jun School of Artificial Intelligence and Computer Science Jiangnan University Wuxi 214122 China

Discriminative correlation filters (DCF) with powerful feature descriptors have proven to be very effective for advanced visual object tracking approaches. However, due to the fixed capacity in achieving discriminative learning, existing DCF trackers perform the filter training on a single template extracted by convolutional neural networks (CNN) or hand-crafted descriptors. Such single template learning cannot provide powerful discriminative filters with guaranteed validity under appearance variation. To pinpoint the structural relevance of spatio-temporal appearance to the filtering system, we propose a new tracking algorithm that incorporates the construction of the Grassmannian manifold learning in the DCF formulation. Our method constructs the model appearance within an online updated affine subspace. It enables joint discriminative learning in the origin and basis of the subspace, achieving enhanced discrimination and interpretability of the learned filters. In addition, to improve tracking efficiency, we adaptively integrate online incremental learning to update the obtained manifold. To this end, specific spatio-temporal appearance patterns are dynamically learned during tracking, highlighting relevant variations and alleviating the performance degrading impact of less discriminative representations from a single template. The experimental results obtained on several well-known datasets, i.e., OTB2013, OTB2015, UAV123, and VOT2018, demonstrate the merits of the proposed method and its superiority over the state-of-the-art trackers. © The Author(s) 2023.

关键词： Affine subspace Discriminative model Grassmannian manifold Online tracking visual object tracking

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Channel Graph Regularized Correlation Filters for visual object tracking

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IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY 2022年第2期32卷 715-729页

作者： Jain, Monika Tyagi, Arjun Subramanyam, A., V Denman, Simon Sridharan, Sridha Fookes, Clinton Indraprastha Inst Informat Technol Dept Elect & Commun New Delhi 110020 India Queensland Univ Technol Artificial Intelligence & Vis Technol SAIVT Res P Signal Proc Brisbane Qld 4000 Australia Indraprastha Inst Informat Technol Dept Comp Sci New Delhi 110020 India

Correlation Filters (CF) are a popular choice for visual object tracking due to their efficiency in the frequency domain. Convolutional and hand-crafted features are jointly used when learning a filter, however, these features are not uniformly important when tracking a target. Given this observation, spatial and temporal regularization and attention models have been investigated. However, these models do not consider the interaction between different feature channels. As a result, dissimilar weights are assigned to similar feature channels. To address this issue, we propose a channel attention model and study two different regularization methods for attention. We investigate the application of channel regularization to emphasize important feature channels;and graph regularization which increases the likelihood of similar feature channels obtaining similar weights. The proposed formulation can be efficiently solved via the alternating direction method of multipliers. We first show the advantages of using the proposed channel regularization by demonstrating its performance when applied to two existing CF trackers. This is followed by analyzing the effect of using the proposed channel-graph regularization for CF based tracking. The evaluation is performed on publicly available tracking datasets: OTB100, TC128, VOT-2017, VOT-2019, LaSOT, UAV123, and GOT-10k. Evaluation over multiple challenges and a comparative analysis with existing top-ranked trackers shows that our formulation improves the discriminative power of the learned CF, preventing tracker drift during challenging scenarios.

关键词： Target tracking Correlation visualization Training Feature extraction object tracking Frequency-domain analysis Correlation filter VGG-Net visual object tracking

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Robust visual object tracking Via Adaptive Attribute-Aware Discriminative Correlation Filters

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IEEE TRANSACTIONS ON MULTIMEDIA 2022年 24卷 301-312页

作者： Zhu, Xue-Feng Wu, Xiao-Jun Xu, Tianyang Feng, Zhen-Hua Kittler, Josef Jiangnan Univ Sch Artificial Intelligence & Comp Sci Wuxi 214122 Jiangsu Peoples R China Jiangnan Univ Sch IoT Wuxi Jiangsu Peoples R China Univ Surrey Ctr Vis Speech & Signal Proc Guildford GU2 7XH Surrey England Univ Surrey Dept Comp Sci Guildford GU2 7XH Surrey England

In recent years, attention mechanisms have been widely studied in Discriminative Correlation Filter (DCF) based visual object tracking. To realise spatial attention and discriminative feature mining, existing approaches usually apply regularisation terms to the spatial dimension of multi-channel features. However, these spatial regularisation approaches construct a shared spatial attention pattern for all multi-channel features, without considering the diversity across channels. As each feature map (channel) focuses on a specific visual attribute, a shared spatial attention pattern limits the capability for mining important information from different channels. To address this issue, we advocate channel-specific spatial attention for DCF-based trackers. The key ingredient of the proposed method is an Adaptive Attribute-Aware spatial attention mechanism for constructing a novel DCF-based tracker (A$<^>3$ DCF). To highlight the discriminative elements in each feature map, spatial sparsity is imposed in the filter learning stage, moderated by the prior knowledge regarding the expected concentration of signal energy. In addition, we perform a post processing of the identified spatial patterns to alleviate the impact of less significant channels. The net effect is that the irrelevant and inconsistent channels are removed by the proposed method. The results obtained on a number of well-known benchmarking datasets, including OTB2015, DTB70, UAV123, VOT2018, LaSOT, GOT-10 K and trackingNet, demonstrate the merits of the proposed A(3) DCF tracker, with improved performance compared to the state-of-the-art methods.

关键词： Discriminative correlation filter spatial attention visual attribute visual object tracking

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A Discriminative Single-Shot Segmentation Network for visual object tracking

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IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE 2022年第12期44卷 9742-9755页

作者： Lukezic, Alan Matas, Jiri Kristan, Matej Univ Ljubljana Fac Comp & Informat Sci Ljubljana 1000 Slovenia Czech Tech Univ Fac Elect Engn Ctr Machine Percept Dept Cybernet Prague 16636 Czech Republic

Template-based discriminative trackers are currently the dominant tracking paradigm due to their robustness, but are restricted to bounding box tracking and a limited range of transformation models, which reduces their localization accuracy. We propose a discriminative single-shot segmentation tracker - D3S(2), which narrows the gap between visual object tracking and video object segmentation. A single-shot network applies two target models with complementary geometric properties, one invariant to a broad range of transformations, including non-rigid deformations, the other assuming a rigid object to simultaneously achieve robust online target segmentation. The overall tracking reliability is further increased by decoupling the object and feature scale estimation. Without per-dataset finetuning, and trained only for segmentation as the primary output, D3S(2) outperforms all published trackers on the recent short-term tracking benchmark VOT2020 and performs very close to the state-of-the-art trackers on the GOT-10k, trackingNet, OTB100 and LaSoT. D3S(2) outperforms the leading segmentation tracker SiamMask on video object segmentation benchmarks and performs on par with top video object segmentation algorithms.

关键词： visual object tracking video object segmentation discriminative tracking single-shot segmentation

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Target-Cognisant Siamese Network for Robust visual object tracking *

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PATTERN RECOGNITION LETTERS 2022年 163卷 129-135页

作者： Jiang, Yingjie Song, Xiaoning Xu, Tianyang Feng, Zhenhua Wu, Xiaojun Kittler, Josef Jiangnan Univ Sch Artificial Intelligence & Comp Sci Wuxi 214122 Peoples R China Univ Surrey Dept Comp Sci Guildford GU2 7XH England Univ Surrey Ctr Vis Speech & Signal Proc Guildford GU2 7XH England

Siamese trackers have become the mainstream framework for visual object tracking in recent years. However, the extraction of the template and search space features is disjoint for a Siamese tracker, resulting in a limited interaction between its classification and regression branches. This degrades the model capacity accurately to estimate the target, especially when it exhibits severe appearance variations. To address this problem, this paper presents a target-cognisant Siamese network for robust visual tracking. First, we introduce a new target-cognisant attention block that computes spatial cross-attention between the template and search branches to convey the relevant appearance information before correlation. Second, we advocate two mechanisms to promote the precision of obtained bounding boxes under complex tracking scenarios. Last, we propose a max filtering module to utilise the guidance of the regression branch to filter out potential interfering predictions in the classification map. The experimental results obtained on challenging benchmarks demonstrate the competitive performance of the proposed method.(c) 2022 Elsevier B.V. All rights reserved.

关键词： visual object tracking Siamese network Anchor -free regression

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MP-LN: motion state prediction and localization network for visual object tracking

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visual COMPUTER 2022年第12期38卷 4291-4306页

作者： Fan, Chunxiao Zhang, Runqing Ming, Yue Beijing Univ Posts & Telecommun Beijing Key Lab Work Safety & Intelligent Monitor Sch Elect Engn Beijing Peoples R China

visual object tracking is an important topic in computer vision, where the methods extracting features from the appearance of the object have made a significant progress. However, occlusion and rapid motion cause an incomplete appearance of the object and an incorrect search area in a complex scene, which limits the precision of object localization. In this paper, we propose a novel motion state prediction and localization network, named MP-LN, for visual object tracking, which predicts and translates a reasonable search area depending on the continuous motion state. Specially, we design a motion state prediction model based on the reinforcement learning, which adopts the policy gradients to estimate the target motion and incorporates rewards to enhance the back-propagation of errors for more accurate motion state. After that, we utilize an iterative localization to fine-tune the identification of the target location, reducing the response suppression. Extensive experiments and results demonstrate the effectiveness and advancement of the proposed method on six challenging tracking datasets, DTB70, UAVDT, UAV123, LaSOT, GOT-10k, and OTB2015.

关键词： visual object tracking Correlation filters Reinforcement learning Motion prediction Policy gradient Iterative localization

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Feature Aggregation Networks Based on Dual Attention Capsules for visual object tracking

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IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY 2022年第2期32卷 674-689页

作者： Cao, Yi Ji, Hongbing Zhang, Wenbo Shirani, Shahram Xidian Univ Sch Elect Engn Xian 710071 Peoples R China McMaster Univ Dept Elect & Comp Engn Hamilton ON L8S 4K1 Canada

tracking-by-detection algorithms have considerably enhanced tracking performance with the introduction of recent convolutional neural networks (CNNs). However, most trackers directly exploit standard scalar-output CNN features, which may not capture enough feature encoding information, instead of aggregated CNN features of vector-output form. In this paper, we propose an end-to-end feature aggregation capsule framework. First, based on the existing CNN network, we aggregate a certain number of similar position-aware CNN features into a capsule to model the feature similarity. The acquired vector-level feature capsules (rather than previous scalar-level pointwise features) are utilized for differentiation learning. We then propose a group attention module to better model the entity representation between different capsule groups thus optimizes total discriminative capability. Third, to reduce the prediction interference resulted by the side effect of dimension rising within capsules, we propose a penalty attention module. Such strategy could dynamically adjust values of neurons by estimating whether they are beneficial or harmful to tracking. Experimental results on five representative benchmarks (UAVDT, DTB70, UAV123, VOT2016 and VOT2018) demonstrate the excellent tracking performance of our dual attention based capsule tracker (DACapT). Specially, it exceeds the previous top tracker by 4.6%/1.9% in precision/success evaluations on UAVDT.

关键词： Feature extraction Neurons visualization object tracking Computational modeling Interference Benchmark testing visual object tracking tracking-by-detection feature capsule group attention penalty attention

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visual object tracking for Unmanned Aerial Vehicles Based on the Template-Driven Siamese Network

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REMOTE SENSING 2022年第7期14卷 1584-1584页

作者： Sun, Lifan Yang, Zhe Zhang, Jinjin Fu, Zhumu He, Zishu Henan Univ Sci & Technol Sch Informat Engn Luoyang 471023 Peoples R China Univ Elect Sci & Technol China Sch Informat & Commun Engn Chengdu 611731 Peoples R China

visual object tracking for unmanned aerial vehicles (UAV) is widely used in many fields such as military reconnaissance, search and rescue work, film shooting, and so on. However, the performance of existing methods is still not very satisfactory due to some complex factors including viewpoint changing, background clutters and occlusion. The Siamese trackers, which offer a convenient way of formulating the visual tracking problem as a template matching process, have achieved success in recent visual tracking datasets. Unfortunately, these template match-based trackers cannot adapt well to frequent appearance change in UAV video datasets. To deal with this problem, this paper proposes a template-driven Siamese network (TDSiam), which consists of feature extraction subnetwork, feature fusion subnetwork and bounding box estimation subnetwork. Especially, a template library branch is proposed for the feature extraction subnetwork to adapt to the changeable appearance of the target. In addition, a feature aligned (FA) module is proposed as the core of feature fusion subnetwork, which can fuse information in the form of center alignment. More importantly, a method for occlusion detection is proposed to reduce the noise caused by occlusion. Experiments were conducted on two challenging benchmarks UAV123 and UAV20L, the results verified the more competitive performance of our proposed method compared to the existing algorithms.

关键词： unmanned aerial vehicles visual object tracking Siamese network template library feature-aligned

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visual object tracking via non-local correlation attention learning

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KNOWLEDGE-BASED SYSTEMS 2022年 254卷

作者： Gao, Long Liu, Pan Ning, Jifeng Li, Yunsong Xidian Univ Sch Telecommun Engn Joint Lab High Speed Multisource Image Coding & Pr 2 South Taibai St Xian 710071 Peoples R China Xidian Univ Sch Telecommun Engn State Key Lab Integrated Serv Networks 2 South Taibai St Xian 710071 Peoples R China Northwest A&F Univ Coll Informat Engn Yangling 712100 Peoples R China

Siamese-based trackers have achieved remarkable advancements in performance of visual object tracking. The similarity matrix computed is crucial to Siamese-based tracker. However, the similarity matrix is lack of long-range dependency information which may lead to tracking drift on challenging scenes, like significant deformation, background clutter and occlusion. To address the above issue, this paper proposes a Siamese network with non-local correlation attention (SiamNCA). First, a non-local correlation attention module is proposed to integrate the long-range information into the similarity matrix, and give each sample in the search patch a weight based on their similarity to the template. Second, bi-directional features fusion module is introduced to fuse different similarity matrixes obtained with different level features. Finally, comprehensive experiments on representative tracking benchmarks, including OTB2015, VOT-2018, LaSOT and GOT-10k, reveal that the two modules can improve the performance of the baseline method in challenging scenes, and SiamNCA achieves state-of-art. For the average running speed, SiamNCA can achieve 43 FPS in real time. (c) 2022 Elsevier B.V. All rights reserved.

关键词： visual object tracking Multiple features fusion Siamese network Anchor -free

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