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 991–1000 of 1322 papers

Title	Date	Tasks	Status
Yet it moves: Learning from Generic Motions to Generate IMU data from YouTube videos	Nov 23, 2020	Activity RecognitionHuman Activity Recognition	—Unverified
Your Day in Your Pocket: Complex Activity Recognition from Smartphone Accelerometers	Jan 17, 2023	Activity RecognitionBinary Classification	—Unverified
Zero-Shot Activity Recognition with Videos	Jan 22, 2020	Action RecognitionActivity Recognition	—Unverified
DASZL: Dynamic Action Signatures for Zero-shot Learning	Dec 8, 2019	Action DetectionActivity Detection	—Unverified
ZKP-FedEval: Verifiable and Privacy-Preserving Federated Evaluation using Zero-Knowledge Proofs	Jul 15, 2025	Activity RecognitionFederated Learning	—Unverified
Smart Laptop Bag with Machine Learning for Activity Recognition	Apr 14, 2019	Activity RecognitionBIG-bench Machine Learning	—Unverified
Zone-based Federated Learning for Mobile Sensing Data	Mar 10, 2023	Activity RecognitionFederated Learning	—Unverified
Human Activity Recognition Using Visual Object Detection	May 2, 2019	Activity RecognitionBIG-bench Machine Learning	—Unverified
Differential Recurrent Neural Network and its Application for Human Activity Recognition	May 9, 2019	Activity RecognitionHuman Activity Recognition	—Unverified
Multi-Modal Recognition of Worker Activity for Human-Centered Intelligent Manufacturing	Aug 20, 2019	Activity PredictionActivity 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