Activity Recognition

Human Activity Recognition is the problem of identifying events performed by humans given a video input. It is formulated as a binary (or multiclass) classification problem of outputting activity class labels. Activity Recognition is an important problem with many societal applications including smart surveillance, video search/retrieval, intelligent robots, and other monitoring systems.

Source: Learning Latent Sub-events in Activity Videos Using Temporal Attention Filters

Papers

Recently Added Most Hyped Most Active Needs Verification Most Verified

Showing 641–650 of 1322 papers

Title	Date	Tasks	Status	Hype
MuMu: Cooperative Multitask Learning-based Guided Multimodal Fusion	Feb 22, 2022	Activity RecognitionHuman Activity Recognition	—Unverified	0
CROMOSim: A Deep Learning-based Cross-modality Inertial Measurement Simulator	Feb 21, 2022	Activity RecognitionData Augmentation	—Unverified	0
Integrated Human Activity Sensing and Communications	Feb 19, 2022	Activity RecognitionHuman Activity Recognition	—Unverified	0
FLAME: Federated Learning Across Multi-device Environments	Feb 17, 2022	Activity RecognitionFederated Learning	—Unverified	0
Multi-View Fusion Transformer for Sensor-Based Human Activity Recognition	Feb 16, 2022	Activity RecognitionHuman Activity Recognition	—Unverified	0
A Prospective Approach for Human-to-Human Interaction Recognition from Wi-Fi Channel Data using Attention Bidirectional Gated Recurrent Neural Network with GUI Application Implementation	Feb 16, 2022	Activity RecognitionHuman Activity Recognition	—Unverified	0
Domain Adaptation with Representation Learning and Nonlinear Relation for Time Series	Feb 15, 2022	Activity RecognitionDomain Adaptation	CodeCode Available	0
Learning Disentangled Behaviour Patterns for Wearable-based Human Activity Recognition	Feb 15, 2022	Activity RecognitionDisentanglement	CodeCode Available	1
Video2IMU: Realistic IMU features and signals from videos	Feb 14, 2022	Activity RecognitionHuman Activity Recognition	—Unverified	0
What Makes Good Contrastive Learning on Small-Scale Wearable-based Tasks?	Feb 12, 2022	Activity RecognitionContrastive Learning	CodeCode Available	1

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All datasets RWF-2000 Stanford40 First-Person Hand Action Benchmark Self-stimulatory Behavior Dataset

Benchmark Results

#	Model	Metric	Claimed	Verified	Status
1	Structured Keypoint Pooling	Accuracy	93.4	—	Unverified
2	Semi-Supervised Hard Attention (SSHA); pretrained on Deepmind Kinetics dataset	Accuracy	90.4	—	Unverified
3	Human Skeletons + Change Detection	Accuracy	90.25	—	Unverified
4	Separable Convolutional LSTM	Accuracy	89.75	—	Unverified
5	SPIL Convolution	Accuracy	89.3	—	Unverified
6	Flow Gated Network	Accuracy	87.25	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	FocusCLIP	Top-3 Accuracy (%)	10.47	—	Unverified
2	CLIP	Top-3 Accuracy (%)	6.49	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	Boutaleb et al.	1:1 Accuracy	97.91	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	all-landmark-model	Activity Recognition	0.76	—	Unverified