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 751–760 of 1322 papers

Title	Date	Tasks	Status
SALIENCE: An Unsupervised User Adaptation Model for Multiple Wearable Sensors Based Human Activity Recognition	Aug 17, 2021	Activity RecognitionHuman Activity Recognition	CodeCode Available
Temporal Action Segmentation with High-level Complex Activity Labels	Aug 15, 2021	Action RecognitionAction Segmentation	—Unverified
AdaRNN: Adaptive Learning and Forecasting of Time Series	Aug 10, 2021	Activity RecognitionFinancial Analysis	CodeCode Available
Pose is all you need: The pose only group activity recognition system (POGARS)	Aug 9, 2021	Action ClassificationActivity Prediction	—Unverified
Non-local Graph Convolutional Network for joint Activity Recognition and Motion Prediction	Aug 3, 2021	Activity RecognitionDecoder	—Unverified
Unsupervised Deep Anomaly Detection for Multi-Sensor Time-Series Signals	Jul 27, 2021	Activity RecognitionAnomaly Detection	—Unverified
Real-Time Activity Recognition and Intention Recognition Using a Vision-based Embedded System	Jul 27, 2021	Activity RecognitionIntent Detection	—Unverified
Neural Style Transfer Enhanced Training Support For Human Activity Recognition	Jul 27, 2021	Activity RecognitionData Augmentation	—Unverified
A Neurorobotics Approach to Behaviour Selection based on Human Activity Recognition	Jul 27, 2021	Activity RecognitionDecision Making	—Unverified
Inference for Change Points in High Dimensional Mean Shift Models	Jul 19, 2021	Activity Recognitionvalid	—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