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 931–940 of 1322 papers

Title	Date	Tasks	Status
MESEN: Exploit Multimodal Data to Design Unimodal Human Activity Recognition with Few Labels	Apr 2, 2024	Activity RecognitionContrastive Learning	—Unverified
Meta-Decomposition: Dynamic Segmentation Approach Selection in IoT-based Activity Recognition	Apr 17, 2024	Activity RecognitionSegmentation	—Unverified
Meta-Learning for Few-Shot Time Series Classification	Sep 13, 2019	Activity RecognitionClassification	—Unverified
Metric-based multimodal meta-learning for human movement identification via footstep recognition	Nov 15, 2021	Activity RecognitionContrastive Learning	—Unverified
MEVA: A Large-Scale Multiview, Multimodal Video Dataset for Activity Detection	Dec 2, 2020	Action DetectionActivity Detection	—Unverified
MIcro-Surgical Anastomose Workflow recognition challenge report	Mar 24, 2021	Activity RecognitionManagement	—Unverified
Millimeter Wave Radar-based Human Activity Recognition for Healthcare Monitoring Robot	May 3, 2024	Activity RecognitionEdge-computing	—Unverified
Mitigating Class Boundary Label Uncertainty to Reduce Both Model Bias and Variance	Feb 23, 2020	Activity RecognitionClassification	—Unverified
MLP-AIR: An Efficient MLP-Based Method for Actor Interaction Relation Learning in Group Activity Recognition	Apr 18, 2023	Activity RecognitionGroup Activity Recognition	—Unverified
mmID: High-Resolution mmWave Imaging for Human Identification	Feb 1, 2024	Activity RecognitionOverall - Test	—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