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End-to-end Flow Correlation Tracking with Spatial-temporal Attention [article]

Zheng Zhu, Wei Wu, Wei Zou, Junjie Yan
2018 arXiv   pre-print
For adaptive aggregation, we propose a novel spatial-temporal attention mechanism.  ...  To the best of our knowledge, this is the first work to jointly train flow and tracking task in a deep learning framework.  ...  End-to-end flow correlation tracking In this section, flow correlation network is given at first to describe the overall training architecture.  ... 
arXiv:1711.01124v4 fatcat:wd3zarrdpfepvojiuiu5vgmnvu

End-to-End Flow Correlation Tracking with Spatial-Temporal Attention

Zheng Zhu, Wei Wu, Wei Zou, Junjie Yan
2018 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition  
Discriminative correlation filters (DCF) with deep convolutional features have achieved favorable performance in recent tracking benchmarks.  ...  To the best of our knowledge, this is the first work to jointly train flow and tracking task in deep learning framework.  ...  End-to-end flow correlation tracking In this section, flow correlation network is given at first to describe the overall training architecture.  ... 
doi:10.1109/cvpr.2018.00064 dblp:conf/cvpr/ZhuWZY18 fatcat:rkxgrswoebbzzkhbvaiyfsiy34

Real-Time Object Tracking Algorithm Based on Siamese Network

Wenjun Zhao, Miaolei Deng, Cong Cheng, Dexian Zhang
2022 Applied Sciences  
Simultaneously, we adopt spatial attention as well as channel attention to effectively restrain the ambient noise, stress the target area, and better extract the features of the given object, so that the  ...  We employ the optical flow network based on the pyramid correlation mapping to evaluate the movement information of the target in two contiguous frames, to increase the accuracy of the feature representation  ...  Siamese Network-Based Tracking An end-to-end network is utilized with the intention of improving tracking performance.  ... 
doi:10.3390/app12147338 fatcat:w52qvgkl7bd4hitv25mgjyusba

Multiple Object Tracking with Correlation Learning [article]

Qiang Wang, Yun Zheng, Pan Pan, Yinghui Xu
2021 arXiv   pre-print
Instead, our paper proposes a learnable correlation operator to establish frame-to-frame matches over convolutional feature maps in the different layers to align and propagate temporal context.  ...  To incorporate the spatial layout, we propose to exploit the local correlation module to model the topological relationship between targets and their surrounding environment, which can enhance the discriminative  ...  Tracking with Optical Flow. FlowTrack [60] introduces optical flow to predict the target location.  ... 
arXiv:2104.03541v1 fatcat:2bektgwrorbwbi72ll4i4wyc2m

Real Time Visual Tracking using Spatial-Aware Temporal Aggregation Network [article]

Tao Hu, Lichao Huang, Xianming Liu, Han Shen
2019 arXiv   pre-print
This paper proposes a correlation filter based tracking method which aggregates historical features in a spatial-aligned and scale-aware paradigm.  ...  However, the lack of exploitation on temporal information prevents tracking algorithms from adapting to appearances changing or resisting to drift.  ...  Conclusion In this work, we propose a end-to-end spatial-aware Temporal Aggregation Network for Visual Object Tracking(SATA) which makes use of the rich information from multi-scale and variable-length  ... 
arXiv:1908.00692v1 fatcat:acwywix2jbgkbicu6ysgordjfu

15 Keypoints Is All You Need

Michael Snower, Asim Kadav, Farley Lai, Hans Peter Graf
2020 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)  
We present an efficient multi-person pose tracking method, KeyTrack, that only relies on keypoint information without using any RGB or optical flow information to track human keypoints in real-time.  ...  However, existing pose tracking methods are unable to accurately model temporal relationships and require significant computation, often computing the tracks offline.  ...  We do not compare with IoU because, GCN and optical flow [35] , [54] have shown to outperform it, nor do we compare to the network from [40] because it is trained in an end-to-end fashion.  ... 
doi:10.1109/cvpr42600.2020.00677 dblp:conf/cvpr/SnowerKLG20 fatcat:jd62ivahirfxjjbs4osug5xrge

TRAT: Tracking by Attention Using Spatio-Temporal Features [article]

Hasan Saribas, Hakan Cevikalp, Okan Köpüklü, Bedirhan Uzun
2020 arXiv   pre-print
The features returned by the two networks are then fused with attention based Feature Aggregation Module (FAM). Since the whole architecture is unified, it can be trained end-to-end.  ...  In this paper, we propose a two-stream deep neural network tracker that uses both spatial and temporal features.  ...  As a result, special attention must be given to combine spatial and temporal information during online tracking.  ... 
arXiv:2011.09524v1 fatcat:u32znmfjrzae5dmj7rtbjqzon4

Revisiting Spatial-Temporal Similarity: A Deep Learning Framework for Traffic Prediction [article]

Huaxiu Yao, Xianfeng Tang, Hua Wei, Guanjie Zheng, Zhenhui Li
2018 arXiv   pre-print
To address these two issues, we propose a novel Spatial-Temporal Dynamic Network (STDN), in which a flow gating mechanism is introduced to learn the dynamic similarity between locations, and a periodically  ...  shifted attention mechanism is designed to handle long-term periodic temporal shifting.  ...  Our approach tracks the dynamic spatial similarity between regions by flow gating mechanism and temporal periodic similarity by periodically shifted attention mechanism.  ... 
arXiv:1803.01254v2 fatcat:vcfzjbavszeozm5otdhgh4rhv4

Revisiting Spatial-Temporal Similarity: A Deep Learning Framework for Traffic Prediction

Huaxiu Yao, Xianfeng Tang, Hua Wei, Guanjie Zheng, Zhenhui Li
2019 PROCEEDINGS OF THE THIRTIETH AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE AND THE TWENTY-EIGHTH INNOVATIVE APPLICATIONS OF ARTIFICIAL INTELLIGENCE CONFERENCE  
To address these two issues, we propose a novel Spatial-Temporal Dynamic Network (STDN), in which a flow gating mechanism is introduced to learn the dynamic similarity between locations, and a periodically  ...  shifted attention mechanism is designed to handle long-term periodic temporal shifting.  ...  Our approach tracks the dynamic spatial similarity between regions by flow gating mechanism and temporal periodic similarity by periodically shifted attention mechanism.  ... 
doi:10.1609/aaai.v33i01.33015668 fatcat:q5xf3vs5z5a7vbakuut4lf7fyq

Recent Advances in Embedding Methods for Multi-Object Tracking: A Survey [article]

Gaoang Wang, Mingli Song, Jenq-Neng Hwang
2022 arXiv   pre-print
Multi-object tracking (MOT) aims to associate target objects across video frames in order to obtain entire moving trajectories.  ...  cross-frame joint embedding, correlation embedding, sequential embedding, tracklet embedding, and cross-track relational embedding.  ...  Specifically, [165] learns spatial and temporal correlations with neighborhood regions for embedding. [170] measures the correlation with the attention mechanism through Transformer architecture. [244  ... 
arXiv:2205.10766v1 fatcat:p7s7lnnlsnadrhsdcmwlg7msfy

Learning Attentions: Residual Attentional Siamese Network for High Performance Online Visual Tracking

Qiang Wang, Zhu Teng, Junliang Xing, Jin Gao, Weiming Hu, Stephen Maybank
2018 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition  
The proposed deep architecture is trained from end to end and takes full advantage of the rich spatial temporal information to achieve robust visual tracking.  ...  The RASNet model reformulates the correlation filter within a Siamese tracking framework, and introduces different kinds of the attention mechanisms to adapt the model without updating the model online  ...  Besides the one-pass evaluation(OPE), temporal robustness evaluation (TRE) and spatial robustness evaluation (SRE) are reported to examine the network sensitivity to the initialization temporally and spatially  ... 
doi:10.1109/cvpr.2018.00510 dblp:conf/cvpr/WangTXGHM18 fatcat:5f2p5jhp55dzxigay5tqhidvza

Towards Accurate Vehicle Behaviour Classification With Multi-Relational Graph Convolutional Networks [article]

Sravan Mylavarapu, Mahtab Sandhu, Priyesh Vijayan, K Madhava Krishna, Balaraman Ravindran, Anoop Namboodiri
2020 arXiv   pre-print
A monocular sequence along with scene semantics, optical flow and object labels are used to get spatial information about the object (vehicle) of interest and other objects (semantically contiguous set  ...  This spatial information is encoded by a Multi-Relational Graph Convolutional Network (MR-GCN), and a temporal sequence of such encodings is fed to a recurrent network to label vehicle behaviours.  ...  The temporal part makes use of flow to track the progress of per frame interobject relations over time.  ... 
arXiv:2002.00786v3 fatcat:yzvkzjxbynbgje462c4m6y7te4

15 Keypoints Is All You Need [article]

Michael Snower, Asim Kadav, Farley Lai, Hans Peter Graf
2020 arXiv   pre-print
We present an efficient Multi-person Pose Tracking method, KeyTrack, that only relies on keypoint information without using any RGB or optical flow information to track human keypoints in real-time.  ...  However, existing pose tracking methods are unable to accurately model temporal relationships and require significant computation, often computing the tracks offline.  ...  We do not compare with IoU because our other baselines, GCN and optical flow [37] , [57] have shown to outperform it, nor do we compare to the network from [42] because it is trained in an end-to-end  ... 
arXiv:1912.02323v2 fatcat:ogyl37mfazeynb4o66hh6caji4

Graph Convolutional Tracking

Junyu Gao, Tianzhu Zhang, Changsheng Xu
2019 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)  
Here, we adopt a spatial-temporal GCN to model the structured representation of historical target exemplars.  ...  To comprehensively leverage the spatial-temporal structure of historical target exemplars and get benefit from the context information, in this work, we present a novel Graph Convolutional Tracking (GCT  ...  In this paper, we propose an end-to-end Graph Convolutional Tracking (GCT) method based on a siamese framework, which can jointly consider both the spatial-temporal target appearance structure of historical  ... 
doi:10.1109/cvpr.2019.00478 dblp:conf/cvpr/GaoZX19 fatcat:gbvsjl2szjccnciwhajkynipwe

Transformer Meets Tracker: Exploiting Temporal Context for Robust Visual Tracking [article]

Ning Wang and Wengang Zhou and Jie Wang and Houqaing Li
2021 arXiv   pre-print
Our transformer-assisted tracking framework is neat and trained in an end-to-end manner.  ...  With the proposed transformer, a simple Siamese matching approach is able to outperform the current top-performing trackers.  ...  This mechanism propagates temporally collected spatial attentions to highlight the target area.  ... 
arXiv:2103.11681v2 fatcat:qhfedgmulbefrmx6trgjvavtge
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