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 721–730 of 1322 papers

Title	Date	Tasks	Status
Plots Unlock Time-Series Understanding in Multimodal Models	Oct 3, 2024	Activity RecognitionTime Series	—Unverified
Pose2Gaze: Eye-body Coordination during Daily Activities for Gaze Prediction from Full-body Poses	Dec 19, 2023	Activity RecognitionGaze Prediction	—Unverified
Pose-conditioned Spatio-Temporal Attention for Human Action Recognition	Mar 29, 2017	Action RecognitionActivity Recognition	—Unverified
Pose is all you need: The pose only group activity recognition system (POGARS)	Aug 9, 2021	Action ClassificationActivity Prediction	—Unverified
Poselet Key-Framing: A Model for Human Activity Recognition	Jun 1, 2013	Activity RecognitionHuman Activity Recognition	—Unverified
PosePilot: An Edge-AI Solution for Posture Correction in Physical Exercises	May 25, 2025	Activity RecognitionComputational Efficiency	—Unverified
Pose Uncertainty Aware Movement Synchrony Estimation via Spatial-Temporal Graph Transformer	Aug 1, 2022	Activity RecognitionContrastive Learning	—Unverified
Post-training Iterative Hierarchical Data Augmentation for Deep Networks	Dec 1, 2020	Activity RecognitionData Augmentation	—Unverified
Pragmatic classification of movement primitives for stroke rehabilitation	Feb 22, 2019	Activity RecognitionClassification	—Unverified
Predicting Human Depression with Hybrid Data Acquisition utilizing Physical Activity Sensing and Social Media Feeds	May 28, 2025	Activity RecognitionSentiment Analysis	—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