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Indoor Future Person Localization from an Egocentric Wearable Camera
Indoor Future Person Localization from an Egocentric Wearable Camera
- Source :
- 2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)
- Publication Year :
- 2021
- Publisher :
- arXiv, 2021.
-
Abstract
- Accurate prediction of future person location and movement trajectory from an egocentric wearable camera can benefit a wide range of applications, such as assisting visually impaired people in navigation, and the development of mobility assistance for people with disability. In this work, a new egocentric dataset was constructed using a wearable camera, with 8,250 short clips of a targeted person either walking 1) toward, 2) away, or 3) across the camera wearer in indoor environments, or 4) staying still in the scene, and 13,817 person bounding boxes were manually labelled. Apart from the bounding boxes, the dataset also contains the estimated pose of the targeted person as well as the IMU signal of the wearable camera at each time point. An LSTM-based encoder-decoder framework was designed to predict the future location and movement trajectory of the targeted person in this egocentric setting. Extensive experiments have been conducted on the new dataset, and have shown that the proposed method is able to reliably and better predict future person location and trajectory in egocentric videos captured by the wearable camera compared to three baselines.<br />Comment: accepted as conference paper in 2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)
- Subjects :
- FOS: Computer and information sciences
Artificial Intelligence (cs.AI)
Computer Science - Artificial Intelligence
Computer Vision and Pattern Recognition (cs.CV)
Computer Science - Computer Vision and Pattern Recognition
ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION
cs.AI
cs.CV
Subjects
Details
- Database :
- OpenAIRE
- Journal :
- 2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)
- Accession number :
- edsair.doi.dedup.....24045c478932b4bcfcb5eeb5129369b7
- Full Text :
- https://doi.org/10.48550/arxiv.2103.04019