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 451–460 of 1322 papers

Title	Date	Tasks	Status
DIVERSIFY: A General Framework for Time Series Out-of-distribution Detection and Generalization	Aug 4, 2023	Activity RecognitionGesture Recognition	—Unverified
Enabling Edge Cloud Intelligence for Activity Learning in Smart Home	May 14, 2020	Activity Recognition	—Unverified
Enabling Machine Learning Across Heterogeneous Sensor Networks with Graph Autoencoders	Dec 12, 2019	Activity RecognitionAnomaly Detection	—Unverified
Encoding Based Saliency Detection for Videos and Images	Jun 1, 2015	Action DetectionActivity Detection	—Unverified
Energy Expenditure Estimation Through Daily Activity Recognition Using a Smart-phone	Sep 8, 2020	Activity RecognitionReinforcement Learning (RL)	—Unverified
Enforcing Fundamental Relations via Adversarial Attacks on Input Parameter Correlations	Jan 9, 2025	Activity RecognitionAdversarial Attack	—Unverified
Diverse Intra- and Inter-Domain Activity Style Fusion for Cross-Person Generalization in Activity Recognition	Jun 7, 2024	Activity RecognitionDiversity	—Unverified
WearableMil: An End-to-End Framework for Military Activity Recognition and Performance Monitoring	Oct 7, 2024	Activity RecognitionCloud Computing	—Unverified
Enhancing Smart Environments with Context-Aware Chatbots using Large Language Models	Feb 20, 2025	Activity RecognitionChatbot	—Unverified
Activity Recognition based on a Magnitude-Orientation Stream Network	Aug 22, 2017	Activity RecognitionOptical Flow Estimation	—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