IndEgo: A Dataset of Industrial Scenarios and Collaborative Work for Egocentric Assistants
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arXiv
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| Autori principali: | , , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866914169940344832 |
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| author | Chavan, Vivek Imgrund, Yasmina Dao, Tung Bai, Sanwantri Wang, Bosong Lu, Ze Heimann, Oliver Krüger, Jörg |
| author_facet | Chavan, Vivek Imgrund, Yasmina Dao, Tung Bai, Sanwantri Wang, Bosong Lu, Ze Heimann, Oliver Krüger, Jörg |
| contents | We introduce IndEgo, a multimodal egocentric and exocentric dataset addressing common industrial tasks, including assembly/disassembly, logistics and organisation, inspection and repair, woodworking, and others. The dataset contains 3,460 egocentric recordings (approximately 197 hours), along with 1,092 exocentric recordings (approximately 97 hours). A key focus of the dataset is collaborative work, where two workers jointly perform cognitively and physically intensive tasks. The egocentric recordings include rich multimodal data and added context via eye gaze, narration, sound, motion, and others. We provide detailed annotations (actions, summaries, mistake annotations, narrations), metadata, processed outputs (eye gaze, hand pose, semi-dense point cloud), and benchmarks on procedural and non-procedural task understanding, Mistake Detection, and reasoning-based Question Answering. Baseline evaluations for Mistake Detection, Question Answering and collaborative task understanding show that the dataset presents a challenge for the state-of-the-art multimodal models. Our dataset is available at: https://huggingface.co/datasets/FraunhoferIPK/IndEgo |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2511_19684 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | IndEgo: A Dataset of Industrial Scenarios and Collaborative Work for Egocentric Assistants Chavan, Vivek Imgrund, Yasmina Dao, Tung Bai, Sanwantri Wang, Bosong Lu, Ze Heimann, Oliver Krüger, Jörg Computer Vision and Pattern Recognition Artificial Intelligence Human-Computer Interaction Robotics We introduce IndEgo, a multimodal egocentric and exocentric dataset addressing common industrial tasks, including assembly/disassembly, logistics and organisation, inspection and repair, woodworking, and others. The dataset contains 3,460 egocentric recordings (approximately 197 hours), along with 1,092 exocentric recordings (approximately 97 hours). A key focus of the dataset is collaborative work, where two workers jointly perform cognitively and physically intensive tasks. The egocentric recordings include rich multimodal data and added context via eye gaze, narration, sound, motion, and others. We provide detailed annotations (actions, summaries, mistake annotations, narrations), metadata, processed outputs (eye gaze, hand pose, semi-dense point cloud), and benchmarks on procedural and non-procedural task understanding, Mistake Detection, and reasoning-based Question Answering. Baseline evaluations for Mistake Detection, Question Answering and collaborative task understanding show that the dataset presents a challenge for the state-of-the-art multimodal models. Our dataset is available at: https://huggingface.co/datasets/FraunhoferIPK/IndEgo |
| title | IndEgo: A Dataset of Industrial Scenarios and Collaborative Work for Egocentric Assistants |
| topic | Computer Vision and Pattern Recognition Artificial Intelligence Human-Computer Interaction Robotics |
| url | https://arxiv.org/abs/2511.19684 |