Abstract
![CDATA[This paper presents a long-term object tracking algorithm for event cameras. A first of its kind for these revolutionary cameras, the tracking framework uses a discriminative representation for the object with online learning, and detects and re-tracks the object when it comes back into the field-of-view. One of the key novelties is the use of an event-based local sliding window technique that performs reliably in scenes with cluttered and textured background. In addition, Bayesian bootstrapping is used to assist real-time processing and boost the discriminative power of the object representation. Extensive experiments on a publicly available event camera dataset demonstrates the ability to track and detect arbitrary objects of various shapes and sizes. This is a significant improvement compared to earlier works that simply track objects as long as they are visible under simpler background settings. In other words, when the object re-enters the field-of-view of the camera, a data-driven, global sliding window based detector locates the object under different view-point conditions for subsequent tracking.]]
Original language | English |
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Title of host publication | Proceedings of the 29th British Machine Vision Conference, 3-6 September 2018, Newcastle, UK |
Publisher | Northumbria University |
Number of pages | 12 |
Publication status | Published - 2018 |
Event | British Machine Vision Conference - Duration: 3 Sept 2018 → … |
Conference
Conference | British Machine Vision Conference |
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Period | 3/09/18 → … |
Keywords
- cameras
- algorithms
- neural networks (computer science)
- image processing