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 971–980 of 1322 papers

Title	Date	Tasks	Status
Multi-Modal Unsupervised Pre-Training for Surgical Operating Room Workflow Analysis	Jul 16, 2022	Activity RecognitionSelf-Supervised Learning	—Unverified
Multi-objective Feature Selection in Remote Health Monitoring Applications	Jan 10, 2024	Activity Recognitionfeature selection	—Unverified
Multi-Person Brain Activity Recognition via Comprehensive EEG Signal Analysis	Sep 26, 2017	Activity RecognitionEEG	—Unverified
Multiple Human Association between Top and Horizontal Views by Matching Subjects' Spatial Distributions	Jul 26, 2019	Activity RecognitionHuman Activity Recognition	—Unverified
Multiple object tracking with context awareness	Nov 24, 2014	Activity RecognitionMultiple Object Tracking	—Unverified
Multi-Scale and Multi-Modal Contrastive Learning Network for Biomedical Time Series	Dec 6, 2023	Activity RecognitionContrastive Learning	—Unverified
Multiscale Manifold Warping	Sep 19, 2021	Activity RecognitionDynamic Time Warping	—Unverified
Multi-Scale Supervised Network for Human Pose Estimation	Aug 5, 2018	Activity RecognitionKeypoint Detection	—Unverified
Multi-Stage Based Feature Fusion of Multi-Modal Data for Human Activity Recognition	Nov 8, 2022	Activity RecognitionHuman Activity Recognition	—Unverified
Multi-stage RGB-based Transfer Learning Pipeline for Hand Activity Recognition	Feb 8, 2022	Activity RecognitionTransfer Learning	—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