Name
Abstract | ||
|---|---|---|
This paper presents a novel Reducing-Over-Time (ROT) binary tree structure for event-based vision data and sub-types of the tree structure. A framework is presented using ROT, that takes advantage of the self-balancing and self-pruning nature of the tree structure to extract spatial-temporal information. The ROT framework is paired with an established motion classification technique and performance is evaluated against other state-of-the-art techniques using four datasets. Additionally, the ROT framework as a processing platform is compared with other event-based vision processing platforms in terms of memory usage and is found to be one of the most memory efficient platforms available. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1109/ICPR48806.2021.9412563 | 2020 25TH INTERNATIONAL CONFERENCE ON PATTERN RECOGNITION (ICPR) |
DocType | ISSN | Citations |
Conference | 1051-4651 | 0 |
PageRank | References | Authors |
0.34 | 0 | 6 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Shane Harrigan | 1 | 0 | 0.68 |
| S. A. Coleman | 2 | 40 | 8.50 |
| Dermot Kerr | 3 | 50 | 13.84 |
| Yogarajah Pratheepan | 4 | 5 | 1.10 |
| Zheng Fang | 5 | 24 | 6.55 |
| Chengdong Wu | 6 | 250 | 46.36 |