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 291–300 of 1322 papers

Title	Date	Tasks	Status
A communication efficient distributed learning framework for smart environments	Sep 27, 2021	Activity Recognition	—Unverified
DanHAR: Dual Attention Network For Multimodal Human Activity Recognition Using Wearable Sensors	Jun 25, 2020	Activity RecognitionHard Attention	—Unverified
Dataiku's Solution to SPHERE's Activity Recognition Challenge	Oct 10, 2016	Activity RecognitionBIG-bench Machine Learning	—Unverified
Decoupled Prompt-Adapter Tuning for Continual Activity Recognition	Jul 20, 2024	Action RecognitionActivity Recognition	—Unverified
Cross-user activity recognition using deep domain adaptation with temporal relation information	Mar 12, 2024	Activity RecognitionDomain Adaptation	—Unverified
Adversarial Transferability in Wearable Sensor Systems	Mar 17, 2020	Activity RecognitionBIG-bench Machine Learning	—Unverified
Cross-user activity recognition via temporal relation optimal transport	Mar 12, 2024	Activity RecognitionDomain Adaptation	—Unverified
A Semi-supervised Approach for Activity Recognition from Indoor Trajectory Data	Jan 9, 2023	Activity RecognitionDecision Making	—Unverified
ARN-LSTM: A Multi-Stream Fusion Model for Skeleton-based Action Recognition	Nov 4, 2024	Action RecognitionActivity Recognition	—Unverified
CrowdTransfer: Enabling Crowd Knowledge Transfer in AIoT Community	Jul 9, 2024	Activity RecognitionHuman 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