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 1111–1120 of 1322 papers

Title	Date	Tasks	Status
A Real-time Human Pose Estimation Approach for Optimal Sensor Placement in Sensor-based Human Activity Recognition	Jul 6, 2023	Activity RecognitionHuman Activity Recognition	—Unverified
A Review of Machine Learning Methods Applied to Video Analysis Systems	Dec 8, 2023	Active LearningActivity Recognition	—Unverified
Arianna+: Scalable Human Activity Recognition by Reasoning with a Network of Ontologies	Sep 21, 2018	Activity RecognitionHuman Activity Recognition	—Unverified
ARIC: An Activity Recognition Dataset in Classroom Surveillance Images	Oct 16, 2024	Activity RecognitionContinual Learning	—Unverified
ARN-LSTM: A Multi-Stream Fusion Model for Skeleton-based Action Recognition	Nov 4, 2024	Action RecognitionActivity Recognition	—Unverified
A Semi-supervised Approach for Activity Recognition from Indoor Trajectory Data	Jan 9, 2023	Activity RecognitionDecision Making	—Unverified
AssembleNet++: Assembling Modality Representations via Attention Connections - Supplementary Material -	Aug 1, 2020	Activity Recognition	—Unverified
Assessing the Impact of Sampling Irregularity in Time Series Data: Human Activity Recognition As A Case Study	Jan 25, 2025	Activity RecognitionHuman Activity Recognition	—Unverified
Assessing the State of Self-Supervised Human Activity Recognition using Wearables	Feb 22, 2022	Activity RecognitionDomain Adaptation	—Unverified
Integrated Human Activity Sensing and Communications	Feb 19, 2022	Activity RecognitionHuman Activity Recognition	—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