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 311–320 of 1322 papers

Title	Date	Tasks	Status	Score
DeepConvContext: A Multi-Scale Approach to Timeseries Classification in Human Activity Recognition	May 27, 2025	Action LocalizationActivity Recognition	CodeCode Available	5
Deep Learning for Sensor-based Activity Recognition: A Survey	Jul 12, 2017	Activity RecognitionDeep Learning	CodeCode Available	5
Data Augmentation Techniques for Cross-Domain WiFi CSI-based Human Activity Recognition	Jan 1, 2024	Activity RecognitionData Augmentation	CodeCode Available	5
DECOMPL: Decompositional Learning with Attention Pooling for Group Activity Recognition from a Single Volleyball Image	Mar 11, 2023	Activity RecognitionGroup Activity Recognition	CodeCode Available	5
Custom Dual Transportation Mode Detection by Smartphone Devices Exploiting Sensor Diversity	Oct 12, 2018	Activity RecognitionBenchmarking	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
Adaptive Client Selection with Personalization for Communication Efficient Federated Learning	Nov 26, 2024	Activity RecognitionFederated Learning	CodeCode Available	5
Incremental Learning of Event Definitions with Inductive Logic Programming	Feb 24, 2014	Activity RecognitionIncremental Learning	CodeCode Available	5
Decomposing and Fusing Intra- and Inter-Sensor Spatio-Temporal Signal for Multi-Sensor Wearable Human Activity Recognition	Jan 19, 2025	Activity RecognitionComputational Efficiency	CodeCode Available	5
Domain Adaptation with Representation Learning and Nonlinear Relation for Time Series	Feb 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