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 71–80 of 1322 papers

Title	Date	Tasks	Status	Hype
A Review of Deep Learning Methods for Photoplethysmography Data	Jan 23, 2024	Activity RecognitionDeep Learning	CodeCode Available	1
Contrastive Learning with Cross-Modal Knowledge Mining for Multimodal Human Activity Recognition	May 20, 2022	Activity RecognitionContrastive Learning	CodeCode Available	1
CubeLearn: End-to-end Learning for Human Motion Recognition from Raw mmWave Radar Signals	Nov 7, 2021	Activity Recognition	CodeCode Available	1
DANA: Dimension-Adaptive Neural Architecture for Multivariate Sensor Data	Aug 5, 2020	Activity RecognitionHuman Activity Recognition	CodeCode Available	1
Autoregressive Adaptive Hypergraph Transformer for Skeleton-based Activity Recognition	Nov 8, 2024	Action ClassificationActivity Recognition	CodeCode Available	1
Deep Learning for Time Series Classification and Extrinsic Regression: A Current Survey	Feb 6, 2023	Activity RecognitionClassification	CodeCode Available	1
Action-slot: Visual Action-centric Representations for Multi-label Atomic Activity Recognition in Traffic Scenes	Nov 29, 2023	Action RecognitionActivity Recognition	CodeCode Available	1
Deep Unsupervised Domain Adaptation for Time Series Classification: a Benchmark	Dec 15, 2023	Activity RecognitionDomain Adaptation	CodeCode Available	1
ESPRESSO: Entropy and ShaPe awaRe timE-Series SegmentatiOn for processing heterogeneous sensor data	Jul 24, 2020	Activity RecognitionChange Point Detection	CodeCode Available	1
Comparing Self-Supervised Learning Techniques for Wearable Human Activity Recognition	Apr 8, 2024	Activity RecognitionHuman Activity Recognition	CodeCode Available	1

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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