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 251–260 of 1322 papers

Title	Date	Tasks	Status	Hype
CODA: A COst-efficient Test-time Domain Adaptation Mechanism for HAR	Mar 22, 2024	Active LearningActivity Recognition	—Unverified	0
Spatio-Temporal Proximity-Aware Dual-Path Model for Panoramic Activity Recognition	Mar 21, 2024	Activity Recognition	—Unverified	0
A Survey of IMU Based Cross-Modal Transfer Learning in Human Activity Recognition	Mar 17, 2024	Action RecognitionActivity Recognition	—Unverified	0
NeuFlow: Real-time, High-accuracy Optical Flow Estimation on Robots Using Edge Devices	Mar 15, 2024	Activity RecognitionEdge-computing	CodeCode Available	2
Generalized Relevance Learning Grassmann Quantization	Mar 14, 2024	Activity RecognitionFace Recognition	CodeCode Available	0
DiTMoS: Delving into Diverse Tiny-Model Selection on Microcontrollers	Mar 14, 2024	Activity RecognitionDiversity	CodeCode Available	0
P2LHAP:Wearable sensor-based human activity recognition, segmentation and forecast through Patch-to-Label Seq2Seq Transformer	Mar 13, 2024	Activity RecognitionAll	—Unverified	0
Deep Generative Domain Adaptation with Temporal Attention for Cross-User Activity Recognition	Mar 12, 2024	Activity RecognitionDomain Adaptation	—Unverified	0
Cross-user activity recognition using deep domain adaptation with temporal relation information	Mar 12, 2024	Activity RecognitionDomain Adaptation	—Unverified	0
Machine Learning Techniques for Sensor-based Human Activity Recognition with Data Heterogeneity -- A Review	Mar 12, 2024	Activity RecognitionHuman Activity Recognition	—Unverified	0

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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