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Journal Article
Background memory‐assisted zero‐shot video object segmentation for unmanned aerial and ground vehicles
- Authors
- Kimin Yun, Hyung-Il Kim, Kangmin Bae, Jinyoung Moon
- Issue Date
- 2023-10
- Citation
- ETRI Journal, v.45, no.5, pp.795-810
- ISSN
- 1225-6463
- Publisher
- 한국전자통신연구원
- Language
- English
- Type
- Journal Article
- Abstract
- Unmanned aerial vehicles (UAV) and ground vehicles (UGV) require advanced video analytics for various tasks, such as moving object detection and segmentation; this has led to increasing demands for these methods. We propose a zero‐shot video object segmentation method specifically designed for UAV and UGV applications that focuses on the discovery of moving objects in challenging scenarios. This method employs a background memory model that enables training from sparse annotations along the time axis, utilizing temporal modeling of the background to detect moving objects effectively. The proposed method addresses the limitations of the existing state‐of‐the‐art methods for detecting salient objects within images, regardless of their movements. In particular, our method achieved mean and values of 82.7 and 81.2 on the DAVIS'16, respectively. We also conducted extensive ablation studies that highlighted the contributions of various input compositions and combinations of datasets used for training. In future developments, we will integrate the proposed method with additional systems, such as tracking and obstacle avoidance functionalities.
- KSP Keywords
- Future Development, Ground vehicles, Memory Model, Moving Object Detection, Obstacle Avoidance, Temporal modeling, Video analytics, Video object segmentation, existing state, segmentation method, unmanned aerial vehicle
This work is distributed under the term of Korea Open Government License (KOGL)
(Type 4: : Type 1 + Commercial Use Prohibition+Change Prohibition)
(Type 4: : Type 1 + Commercial Use Prohibition+Change Prohibition)
