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 161–170 of 1322 papers

Title	Date	Tasks	Status	Score
A Comparison of Deep Learning and Established Methods for Calf Behaviour Monitoring	Aug 23, 2024	Activity RecognitionHuman Activity Recognition	CodeCode Available	5
Generalized Relevance Learning Grassmann Quantization	Mar 14, 2024	Activity RecognitionFace Recognition	CodeCode Available	5
Group Activity Recognition Using Joint Learning of Individual Action Recognition and People Grouping	Jul 17, 2021	Action RecognitionActivity Recognition	CodeCode Available	5
IoT-Based Real-Time Medical-Related Human Activity Recognition Using Skeletons and Multi-Stage Deep Learning for Healthcare	Jan 13, 2025	Activity RecognitionHuman Activity Recognition	CodeCode Available	5
Exploring Video-Based Driver Activity Recognition under Noisy Labels	Apr 16, 2025	Activity RecognitionDenoising	CodeCode Available	5
Alignment-based conformance checking over probabilistic events	Sep 9, 2022	Activity Recognition	CodeCode Available	5
Explaining Human Activity Recognition with SHAP: Validating Insights with Perturbation and Quantitative Measures	Nov 6, 2024	Action RecognitionActivity Recognition	CodeCode Available	5
Evaluating Spiking Neural Network On Neuromorphic Platform For Human Activity Recognition	Aug 1, 2023	Activity RecognitionHuman Activity Recognition	CodeCode Available	5
Feature engineering workflow for activity recognition from synchronized inertial measurement units	Dec 18, 2019	Activity RecognitionBIG-bench Machine Learning	CodeCode Available	5
Ensemble diverse hypotheses and knowledge distillation for unsupervised cross-subject adaptation	Apr 15, 2022	Activity RecognitionDomain Adaptation	CodeCode Available	5

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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