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| Content Provider | IET Digital Library |
|---|---|
| Author | Hassan, Ehtesham |
| Abstract | Multiple object tracking sets the foundation for many intelligent video applications. The authors present a novel tracking solution using the ability of recurrent neural networks to effectively model complex temporal dynamics between objects irrespective of appearances, pose, occlusions, and illumination. For online tracking, a real-time and accurate association of objects with active tracks poses the major algorithmic challenge. Additionally, re-entry of objects should also be correctly resolved. They follow tracking-by-detection methodology using hierarchical long short term memory (LSTM) network structure for modelling the motion dynamics between objects by learning the fusion of appearance and motion cues. Existing works capture object's perspective for tracking within the detected bounding boxes. They also incorporate object instance segments for track modelling by applying the maskRCNN detector. They present a novel motion coding scheme that anchors the LSTM structure to effectively model the motion and relative position between objects in a single representation scheme. The proposed motion representation and deep features representing objects appearances are fused in an embedded space learned by the hierarchical LSTM structure for predicting the object to track association. The authors present experimental validation of the proposed approach on multiple object tracking challenge datasets and demonstrate that their solution naturally deals with major tracking challenges under all uncertainties. |
| Starting Page | 893 |
| Ending Page | 899 |
| Page Count | 7 |
| Volume Number | 2020 |
| e-ISSN | 20513305 |
| Issue Number | Issue 10, Oct (2020) |
| Alternate Webpage(s) | https://digital-library.theiet.org/content/journals/joe/2020/10 |
| Alternate Webpage(s) | https://digital-library.theiet.org/content/journals/10.1049/joe.2020.0111 |
| Journal | The Journal of Engineering |
| Publisher | The Institution of Engineering and Technology |
| Publisher Date | 2020-07-17 |
| Access Restriction | Open |
| Rights License | Creative Commons Attribution-No Derivs License (http://creativecommons.org/licenses/by-nd/3.0/) |
| Subject Keyword | Active Tracks Bounding Boxes Complex Temporal Dynamics Computer Vision And Image Processing Technique Feature Extraction Feature Fusion Hierarchical Long Short Term Memory Network Structure Hierarchical LSTM Hierarchical LSTM Structure Image Motion Analysis Image Recognition Image Representation Image Segmentation Image Sequence Intelligent Video Application Knowledge Engineering Technique Learning in AI Motion Coding Scheme Motion Cues Motion Dynamics Motion Representation Multiple Object Tracking Challenge Datasets Object Detection Object Instance Segments Object Tracking Online Tracking Recurrent Neural Nets Recurrent Neural Network Relative Position Track Association Track Modelling Tracking-by-detection Methodology Video Signal Processing |
| Content Type | Text |
| Resource Type | Article |
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