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 221–230 of 1322 papers

Title	Date	Tasks	Status	Score
Enhancing Wearable Tap Water Audio Detection through Subclass Annotation in the HD-Epic Dataset	May 27, 2025	Activity RecognitionHuman Activity Recognition	CodeCode Available	5
Analysis of Hand Segmentation in the Wild	Mar 8, 2018	Action RecognitionActivity Recognition	CodeCode Available	5
FAR: Fourier Aerial Video Recognition	Mar 21, 2022	Action RecognitionActivity Recognition	CodeCode Available	5
Human Activity Recognition Based on Wearable Sensor Data: A Standardization of the State-of-the-Art	Jun 13, 2018	Activity RecognitionHuman Activity Recognition	CodeCode Available	5
An Analysis of Parallelized Motion Masking Using Dual-Mode Single Gaussian Models	Feb 16, 2017	Activity RecognitionMotion Detection	CodeCode Available	5
Specifying Weight Priors in Bayesian Deep Neural Networks with Empirical Bayes	Jun 12, 2019	Activity RecognitionAudio Classification	CodeCode Available	5
AssembleNet++: Assembling Modality Representations via Attention Connections	Aug 18, 2020	Action ClassificationActivity Recognition	CodeCode Available	5
Multimodal Explanations: Justifying Decisions and Pointing to the Evidence	Feb 15, 2018	Activity RecognitionExplainable Models	CodeCode Available	5
ASM2TV: An Adaptive Semi-Supervised Multi-Task Multi-View Learning Framework for Human Activity Recognition	May 18, 2021	Activity RecognitionHuman Activity Recognition	CodeCode Available	5
Adversarial Domain Adaptation for Cross-user Activity Recognition Using Diffusion-based Noise-centred Learning	Aug 6, 2024	Activity RecognitionDenoising	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