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

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Showing 151–160 of 1322 papers

Title	Date	Tasks	Status	Score
Fully Convolutional Network Bootstrapped by Word Encoding and Embedding for Activity Recognition in Smart Homes	Dec 1, 2020	Activity RecognitionGeneral Classification	CodeCode Available	5
FAR: Fourier Aerial Video Recognition	Mar 21, 2022	Action RecognitionActivity Recognition	CodeCode Available	5
Generative Pretrained Embedding and Hierarchical Irregular Time Series Representation for Daily Living Activity Recognition	Dec 27, 2024	Activity RecognitionDecoder	CodeCode Available	5
Guidelines for Augmentation Selection in Contrastive Learning for Time Series Classification	Jul 12, 2024	Activity RecognitionContrastive Learning	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
FedAli: Personalized Federated Learning with Aligned Prototypes through Optimal Transport	Nov 15, 2024	Activity RecognitionFederated Learning	CodeCode Available	5
Exploring Video-Based Driver Activity Recognition under Noisy Labels	Apr 16, 2025	Activity RecognitionDenoising	CodeCode Available	5
Robust Explainer Recommendation for Time Series Classification	Jun 8, 2023	Activity RecognitionClassification	CodeCode Available	5
Evaluating Spiking Neural Network On Neuromorphic Platform For Human Activity Recognition	Aug 1, 2023	Activity RecognitionHuman Activity Recognition	CodeCode Available	5
A Matter of Annotation: An Empirical Study on In Situ and Self-Recall Activity Annotations from Wearable Sensors	May 15, 2023	Activity RecognitionHuman Activity Recognition	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