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 801–810 of 1322 papers

Title	Date	Tasks	Status
Efficient data-driven encoding of scene motion using Eccentricity	Mar 3, 2021	Activity RecognitionIntent Recognition	—Unverified
SimHumalator: An Open Source WiFi Based Passive Radar Human Simulator For Activity Recognition	Mar 2, 2021	Activity RecognitionClassification	—Unverified
Physical Activity Recognition Based on a Parallel Approach for an Ensemble of Machine Learning and Deep Learning Classifiers	Mar 2, 2021	Activity RecognitionFeature Engineering	—Unverified
Human Activity Recognition using Deep Learning Models on Smartphones and Smartwatches Sensor Data	Feb 28, 2021	Activity RecognitionHuman Activity Recognition	—Unverified
Multi-Task Temporal Convolutional Networks for Joint Recognition of Surgical Phases and Steps in Gastric Bypass Procedures	Feb 24, 2021	Activity Recognition	—Unverified
Transfer Learning for Future Wireless Networks: A Comprehensive Survey	Feb 15, 2021	Activity RecognitionHuman Activity Recognition	—Unverified
Efficient Multi-stream Temporal Learning and Post-fusion Strategy for 3D Skeleton-based Hand Activity Recognition	Feb 10, 2021	Activity RecognitionSkeleton Based Action Recognition	—Unverified
Improving state estimation through projection post-processing for activity recognition with application to football	Feb 5, 2021	Activity RecognitionClassification	CodeCode Available
AHAR: Adaptive CNN for Energy-efficient Human Activity Recognition in Low-power Edge Devices	Feb 3, 2021	Activity RecognitionCloud Computing	—Unverified
Provably Secure Federated Learning against Malicious Clients	Feb 3, 2021	Activity RecognitionFederated Learning	—Unverified

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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