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 1041–1050 of 1322 papers

Title	Date	Tasks	Status
Adaptive Activity Monitoring with Uncertainty Quantification in Switching Gaussian Process Models	Jan 8, 2019	Activity RecognitionGaussian Processes	—Unverified
Adaptive Feature Processing for Robust Human Activity Recognition on a Novel Multi-Modal Dataset	Jan 9, 2019	Activity RecognitionAutonomous Vehicles	—Unverified
AdaSense: Adaptive Low-Power Sensing and Activity Recognition for Wearable Devices	Jun 10, 2020	Activity RecognitionClassification	—Unverified
A dataset for complex activity recognition withmicro and macro activities in a cooking scenario	Jun 18, 2020	Activity Recognition	—Unverified
A Deep Learning Approach To Multiple Kernel Fusion	Dec 28, 2016	Activity RecognitionDeep Learning	—Unverified
A Deep Learning Framework using Passive WiFi Sensing for Respiration Monitoring	Apr 19, 2017	Activity RecognitionDeep Learning	—Unverified
A Deep Learning Method for Complex Human Activity Recognition Using Virtual Wearable Sensors	Mar 4, 2020	Activity RecognitionDiversity	—Unverified
A Deep Structured Model with Radius-Margin Bound for 3D Human Activity Recognition	Dec 5, 2015	Activity RecognitionHuman Activity Recognition	—Unverified
A distillation-based approach integrating continual learning and federated learning for pervasive services	Sep 9, 2021	Activity RecognitionContinual Learning	—Unverified
Advancing Location-Invariant and Device-Agnostic Motion Activity Recognition on Wearable Devices	Feb 6, 2024	Activity Recognition	—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