We introduce Ego4D, a massive-scale egocentric video dataset and benchmark suite. It offers 3,025 hours of dailylife activity video spanning hundreds of scenarios (household, outdoor, workplace, leisure, etc.) captured by 855 unique camera wearers from 74 worldwide locations and 9 different countries. The approach to collection is designed to uphold rigorous privacy and ethics standards with consenting participants and robust de-identification procedures where relevant. Ego4D dramatically expands the volume of diverse egocentric video footage publicly available to the research community. Portions of the video are accompanied by audio, 3D meshes of the environment, eye gaze, stereo, and/or synchronized videos from multiple egocentric cameras at the same event. Furthermore, we present a host of new benchmark challenges centered around understanding the first-person visual experience in the past (querying an episodic memory), present (analyzing hand-object manipulation, audio-visual conversation, and social interactions), and future (forecasting activities). By publicly sharing this massive annotated dataset and benchmark suite, we aim to push the frontier of first-person perception.

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It is well known that many collisions occur because one ship turns right whilst the other turns left when in close proximity to one another. Little is known as to why this occurs and, although some simulation models have been established using entropy theory, the problem remains unsolved.In this paper, an assessment model for uncertainty is reviewed briefly. The concepts of uncertainty and uncoordination of mariners' behaviour in collision avoidance are discussed. A simulation model in conjunction with a DCPA (distance to the closest point of approach) decision-making model using fuzzy programming is introduced to discuss coordination.

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