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End-to-end Flow Correlation Tracking with Spatial-temporal Attention
[article]
2018
arXiv
pre-print
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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
2018
2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition
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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
2022
Applied Sciences
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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]
2021
arXiv
pre-print
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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]
2019
arXiv
pre-print
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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
2020
2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
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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]
2020
arXiv
pre-print
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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]
2018
arXiv
pre-print
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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
2019
PROCEEDINGS OF THE THIRTIETH AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE AND THE TWENTY-EIGHTH INNOVATIVE APPLICATIONS OF ARTIFICIAL INTELLIGENCE CONFERENCE
Preserved Fulltext
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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]
2022
arXiv
pre-print
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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
2018
2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition
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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]
2020
arXiv
pre-print
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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]
2020
arXiv
pre-print
Preserved Fulltext
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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
2019
2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
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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]
2021
arXiv
pre-print
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2021 pre-print arXiv:2103.11681v1 fatcat:7xniuwa4pnby7cqn3owvn2qzx4
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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