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 891–900 of 1322 papers

Title	Date	Tasks	Status
ARC-Net: Activity Recognition Through Capsules	Jul 6, 2020	Activity RecognitionARC	—Unverified
Joint Learning of Social Groups, Individuals Action and Sub-group Activities in Videos	Jul 6, 2020	Activity RecognitionGroup Activity Recognition	—Unverified
Handling Variable-Dimensional Time Series with Graph Neural Networks	Jul 1, 2020	Activity RecognitionGraph Neural Network	—Unverified
Human Activity Recognition based on Dynamic Spatio-Temporal Relations	Jun 29, 2020	Activity RecognitionHuman Activity Recognition	—Unverified
Automatic Operating Room Surgical Activity Recognition for Robot-Assisted Surgery	Jun 29, 2020	Action RecognitionActivity Recognition	—Unverified
Background Knowledge Injection for Interpretable Sequence Classification	Jun 25, 2020	Activity RecognitionClassification	—Unverified
DanHAR: Dual Attention Network For Multimodal Human Activity Recognition Using Wearable Sensors	Jun 25, 2020	Activity RecognitionHard Attention	—Unverified
A dataset for complex activity recognition withmicro and macro activities in a cooking scenario	Jun 18, 2020	Activity Recognition	—Unverified
Learning-to-Learn Personalised Human Activity Recognition Models	Jun 12, 2020	Activity RecognitionHuman Activity Recognition	—Unverified
AdaSense: Adaptive Low-Power Sensing and Activity Recognition for Wearable Devices	Jun 10, 2020	Activity RecognitionClassification	—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