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 741–750 of 1322 papers

Title	Date	Tasks	Status	Hype
Explainable Activity Recognition for Smart Home Systems	May 20, 2021	Activity RecognitionExplainable artificial intelligence	—Unverified	0
Egocentric Activity Recognition and Localization on a 3D Map	May 20, 2021	Action LocalizationAction Recognition	—Unverified	0
Social Behaviour Understanding using Deep Neural Networks: Development of Social Intelligence Systems	May 20, 2021	Activity RecognitionDepression Detection	—Unverified	0
ASM2TV: An Adaptive Semi-Supervised Multi-Task Multi-View Learning Framework for Human Activity Recognition	May 18, 2021	Activity RecognitionHuman Activity Recognition	CodeCode Available	0
Learning Group Activities from Skeletons without Individual Action Labels	May 14, 2021	Activity RecognitionGroup Activity Recognition	CodeCode Available	1
Event-LSTM: An Unsupervised and Asynchronous Learning-based Representation for Event-based Data	May 10, 2021	Activity RecognitionGesture Recognition	—Unverified	0
Evaluating Deep Neural Network Ensembles by Majority Voting cum Meta-Learning scheme	May 9, 2021	Activity RecognitionHuman Activity Recognition	—Unverified	0
Human Activity Recognition Models in Ontology Networks	May 5, 2021	Activity RecognitionHuman Activity Recognition	—Unverified	0
Activity-Aware Deep Cognitive Fatigue Assessment using Wearables	May 5, 2021	Activity Recognition	—Unverified	0
Three-stream network for enriched Action Recognition	Apr 27, 2021	Action RecognitionActivity Recognition	—Unverified	0

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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