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Concurrent Activity Recognition with Multimodal CNN-LSTM Structure
We introduce a system that recognizes concurrent activities from real-wo...
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Multi-Label Activity Recognition using Activity-specific Features
We introduce an approach to multi-label activity recognition by extracti...
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Group Activity Detection from Trajectory and Video Data in Soccer
Group activity detection in soccer can be done by using either video dat...
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Actor-Transformers for Group Activity Recognition
This paper strives to recognize individual actions and group activities ...
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Multi-agent Attentional Activity Recognition
Multi-modality is an important feature of sensor based activity recognit...
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HAMLET: A Hierarchical Multimodal Attention-based Human Activity Recognition Algorithm
To fluently collaborate with people, robots need the ability to recogniz...
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Augmenting Bag-of-Words: Data-Driven Discovery of Temporal and Structural Information for Activity Recognition
We present data-driven techniques to augment Bag of Words (BoW) models, ...
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Tri-axial Self-Attention for Concurrent Activity Recognition
We present a system for concurrent activity recognition. To extract features associated with different activities, we propose a feature-to-activity attention that maps the extracted global features to sub-features associated with individual activities. To model the temporal associations of individual activities, we propose a transformer-network encoder that models independent temporal associations for each activity. To make the concurrent activity prediction aware of the potential associations between activities, we propose self-attention with an association mask. Our system achieved state-of-the-art or comparable performance on three commonly used concurrent activity detection datasets. Our visualizations demonstrate that our system is able to locate the important spatial-temporal features for final decision making. We also showed that our system can be applied to general multilabel classification problems.
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