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 981–990 of 1322 papers

Title	Date	Tasks	Status
A Lightweight Deep Learning Model for Human Activity Recognition on Edge Devices	Sep 20, 2019	Activity RecognitionDeep Learning	—Unverified
Simultaneous Segmentation and Recognition: Towards more accurate Ego Gesture Recognition	Sep 18, 2019	Activity RecognitionCaption Generation	—Unverified
Meta-Learning for Few-Shot Time Series Classification	Sep 13, 2019	Activity RecognitionClassification	—Unverified
Activity recognition using ST-GCN with 3D motion data	Sep 13, 2019	Activity RecognitionMultimodal Activity Recognition	—Unverified
Extreme Low Resolution Activity Recognition with Confident Spatial-Temporal Attention Transfer	Sep 9, 2019	Activity RecognitionPrivacy Preserving	—Unverified
Can a simple approach identify complex nurse care activity?	Sep 9, 2019	Activity RecognitionMultimodal Activity Recognition	—Unverified
Nurse care activity recognition challenge: summary and results	Sep 9, 2019	Activity RecognitionMultimodal Activity Recognition	—Unverified
Personalizing Smartwatch Based Activity Recognition Using Transfer Learning	Sep 3, 2019	Activity RecognitionTransfer Learning	—Unverified
Temporal Reasoning Graph for Activity Recognition	Aug 27, 2019	Action RecognitionActivity Recognition	—Unverified
Multi-Modal Recognition of Worker Activity for Human-Centered Intelligent Manufacturing	Aug 20, 2019	Activity PredictionActivity 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