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 661–670 of 1322 papers

Title	Date	Tasks	Status
MU-MAE: Multimodal Masked Autoencoders-Based One-Shot Learning	Aug 8, 2024	Activity RecognitionHuman Activity Recognition	—Unverified
MuMu: Cooperative Multitask Learning-based Guided Multimodal Fusion	Feb 22, 2022	Activity RecognitionHuman Activity Recognition	—Unverified
MVSA-Net: Multi-View State-Action Recognition for Robust and Deployable Trajectory Generation	Nov 14, 2023	Action RecognitionActivity Recognition	—Unverified
Natively neuromorphic LMU architecture for encoding-free SNN-based HAR on commercial edge devices	Jul 4, 2024	Activity RecognitionHuman Activity Recognition	—Unverified
Negative Selection by Clustering for Contrastive Learning in Human Activity Recognition	Mar 23, 2022	Activity RecognitionClustering	—Unverified
NERULA: A Dual-Pathway Self-Supervised Learning Framework for Electrocardiogram Signal Analysis	May 21, 2024	Activity RecognitionContrastive Learning	—Unverified
Nested Motion Descriptors	Jun 1, 2015	Activity RecognitionOptical Flow Estimation	—Unverified
Neural Style Transfer Enhanced Training Support For Human Activity Recognition	Jul 27, 2021	Activity RecognitionData Augmentation	—Unverified
Neuro-Symbolic Approaches for Context-Aware Human Activity Recognition	Jun 8, 2023	Activity RecognitionHuman Activity Recognition	—Unverified
New Convex Relaxations for MRF Inference With Unknown Graphs	Oct 1, 2019	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