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231225s2020 xx |||||o 00| ||eng c |
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|a 10.1109/TPAMI.2019.2919301
|2 doi
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|a pubmed24n0992.xml
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|a eng
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| 100 |
1 |
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|a Ramesh, Bharath
|e verfasserin
|4 aut
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| 245 |
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|a DART
|b Distribution Aware Retinal Transform for Event-Based Cameras
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|c 2020
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|a Text
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|a ƒa Online-Ressource
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|a Date Completed 12.02.2021
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|a Date Revised 12.02.2021
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|a published: Print-Electronic
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|a Citation Status PubMed-not-MEDLINE
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|a We introduce a generic visual descriptor, termed as distribution aware retinal transform (DART), that encodes the structural context using log-polar grids for event cameras. The DART descriptor is applied to four different problems, namely object classification, tracking, detection and feature matching: (1) The DART features are directly employed as local descriptors in a bag-of-words classification framework and testing is carried out on four standard event-based object datasets (N-MNIST, MNIST-DVS, CIFAR10-DVS, NCaltech-101); (2) Extending the classification system, tracking is demonstrated using two key novelties: (i) Statistical bootstrapping is leveraged with online learning for overcoming the low-sample problem during the one-shot learning of the tracker, (ii) Cyclical shifts are induced in the log-polar domain of the DART descriptor to achieve robustness to object scale and rotation variations; (3) To solve the long-term object tracking problem, an object detector is designed using the principle of cluster majority voting. The detection scheme is then combined with the tracker to result in a high intersection-over-union score with augmented ground truth annotations on the publicly available event camera dataset; (4) Finally, the event context encoded by DART greatly simplifies the feature correspondence problem, especially for spatio-temporal slices far apart in time, which has not been explicitly tackled in the event-based vision domain
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|a Journal Article
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|a Yang, Hong
|e verfasserin
|4 aut
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| 700 |
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|a Orchard, Garrick
|e verfasserin
|4 aut
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| 700 |
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|a Le Thi, Ngoc Anh
|e verfasserin
|4 aut
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| 700 |
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|a Zhang, Shihao
|e verfasserin
|4 aut
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| 700 |
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|a Xiang, Cheng
|e verfasserin
|4 aut
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| 773 |
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|i Enthalten in
|t IEEE transactions on pattern analysis and machine intelligence
|d 1979
|g 42(2020), 11 vom: 30. Nov., Seite 2767-2780
|w (DE-627)NLM098212257
|x 1939-3539
|7 nnns
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| 773 |
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|g volume:42
|g year:2020
|g number:11
|g day:30
|g month:11
|g pages:2767-2780
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| 856 |
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|u http://dx.doi.org/10.1109/TPAMI.2019.2919301
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