CAMREP- Concordia Action and Motion Repository
원문 초록
Action recognition, motion classification, gait analysis and synthesis are fundamental problems in a number of fields such as computer graphics, bio-mechanics and human computer interaction that generate a large body of research. This type of data is complex because it is inherently multidimensional and has multiple modalities such as video, motion capture data, accelerometer data, etc. While some of this data, such as monocular video are easy to acquire, others are much more difficult and expensive such as motion capture data or multi-view video. This creates a large barrier of entry in the research community for data driven research. We have embarked on creating a new large repository of motion and action data (CAMREP) consisting of several motion and action databases. What makes this database unique is that we use a variety of modalities, enabling multi-modal analysis. Presently, the size of datasets varies with some having a large number of subjects while others having smaller numbers. We have also acquired long capture sequences in a number of cases, making some datasets rather large.
함께 살펴볼 논문
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Are Spatial-Temporal Graph Convolution Networks for Human Action Recognition Over-Parameterized?
Talk is Not Always Cheap: Promoting Wireless Sensing Models with Text Prompts
XRF V2: A Dataset for Action Summarization with Wi-Fi Signals, and IMUs in Phones, Watches, Earbuds, and Glasses
Visual WetlandBirds Dataset: Bird Species Identification and Behavior Recognition in Videos
OccludeNet: A Causal Journey into Mixed-View Actor-Centric Video Action Recognition under Occlusions
Efficient Transfer Learning for Video-language Foundation Models
토론
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