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 1261–1270 of 1322 papers

Title	Date	Tasks	Status
Multi-view Video-Pose Pretraining for Operating Room Surgical Activity Recognition	Feb 19, 2025	Activity Recognition	CodeCode Available
Custom Dual Transportation Mode Detection by Smartphone Devices Exploiting Sensor Diversity	Oct 12, 2018	Activity RecognitionBenchmarking	CodeCode Available
Domain Adaptation with Representation Learning and Nonlinear Relation for Time Series	Feb 15, 2022	Activity RecognitionDomain Adaptation	CodeCode Available
A Novel Skeleton-Based Human Activity Discovery Using Particle Swarm Optimization with Gaussian Mutation	Jan 14, 2022	Activity RecognitionHuman Activity Recognition	CodeCode Available
Interpretable 3D Human Action Analysis with Temporal Convolutional Networks	Apr 14, 2017	3D Action RecognitionAction Analysis	CodeCode Available
Domain Adaptation Under Behavioral and Temporal Shifts for Natural Time Series Mobile Activity Recognition	Jul 10, 2022	Activity RecognitionContrastive Learning	CodeCode Available
Alignment-based conformance checking over probabilistic events	Sep 9, 2022	Activity Recognition	CodeCode Available
Cross-modal Knowledge Distillation for Vision-to-Sensor Action Recognition	Oct 8, 2021	Action RecognitionActivity Recognition	CodeCode Available
Proximity-Based Active Learning on Streaming Data: A Personalized Eating Moment Recognition	Mar 29, 2020	Active LearningActivity Recognition	CodeCode Available
Investigating Enhancements to Contrastive Predictive Coding for Human Activity Recognition	Nov 11, 2022	Activity RecognitionHuman Activity Recognition	CodeCode Available

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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