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 671–680 of 1322 papers

Title	Date	Tasks	Status	Hype
PartImageNet: A Large, High-Quality Dataset of Parts	Dec 2, 2021	Activity RecognitionFew-Shot Learning	CodeCode Available	1
Weakly-guided Self-supervised Pretraining for Temporal Activity Detection	Nov 26, 2021	Action DetectionActivity Detection	CodeCode Available	0
Scene Graph Generation with Geometric Context	Nov 25, 2021	Activity RecognitionGraph Generation	—Unverified	0
Human Activity Recognition Using 3D Orthogonally-projected EfficientNet on Radar Time-Range-Doppler Signature	Nov 24, 2021	Activity RecognitionHuman Activity Recognition	—Unverified	0
Using Language Model to Bootstrap Human Activity Recognition Ambient Sensors Based in Smart Homes	Nov 23, 2021	Activity RecognitionHuman Activity Recognition	CodeCode Available	0
FLSys: Toward an Open Ecosystem for Federated Learning Mobile Apps	Nov 17, 2021	Activity RecognitionData Augmentation	—Unverified	0
Metric-based multimodal meta-learning for human movement identification via footstep recognition	Nov 15, 2021	Activity RecognitionContrastive Learning	—Unverified	0
Classifying Human Activities with Inertial Sensors: A Machine Learning Approach	Nov 9, 2021	Activity RecognitionBIG-bench Machine Learning	—Unverified	0
CubeLearn: End-to-end Learning for Human Motion Recognition from Raw mmWave Radar Signals	Nov 7, 2021	Activity Recognition	CodeCode Available	1
Frugal Machine Learning	Nov 5, 2021	Activity RecognitionBIG-bench Machine Learning	—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