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 1051–1060 of 1322 papers

Title	Date	Tasks	Status
Babel: A Scalable Pre-trained Model for Multi-Modal Sensing via Expandable Modality Alignment	Jul 25, 2024	Activity RecognitionHuman Activity Recognition	—Unverified
Adversarial Deep Feature Extraction Network for User Independent Human Activity Recognition	Oct 23, 2021	Activity RecognitionDecoder	—Unverified
Adversarial Transferability in Wearable Sensor Systems	Mar 17, 2020	Activity RecognitionBIG-bench Machine Learning	—Unverified
A Feature Selection Method for Multi-Dimension Time-Series Data	Apr 22, 2021	Activity Recognitionfeature selection	—Unverified
Affinity-Based Hierarchical Learning of Dependent Concepts for Human Activity Recognition	Apr 11, 2021	Activity RecognitionGeneral Classification	—Unverified
A Fourier Domain Feature Approach for Human Activity Recognition & Fall Detection	Mar 11, 2020	Activity RecognitionData Augmentation	—Unverified
A Framework For Identifying Group Behavior Of Wild Animals	Jul 1, 2019	Activity Recognition	—Unverified
A framework for mining process models from emails logs	Sep 20, 2016	Activity Recognition	—Unverified
AgentSense: Virtual Sensor Data Generation Using LLM Agents in Simulated Home Environments	Jun 13, 2025	Activity RecognitionHuman Activity Recognition	—Unverified
AHAR: Adaptive CNN for Energy-efficient Human Activity Recognition in Low-power Edge Devices	Feb 3, 2021	Activity RecognitionCloud Computing	—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