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 741–750 of 1322 papers

Title	Date	Tasks	Status
Lightweight Transformer in Federated Setting for Human Activity Recognition	Oct 1, 2021	Activity RecognitionFederated Learning	—Unverified
T-WaveNet: A Tree-Structured Wavelet Neural Network for Time Series Signal Analysis	Sep 29, 2021	Activity RecognitionRepresentation Learning	—Unverified
DIVERSIFY to Generalize: Learning Generalized Representations for Time Series Classification	Sep 29, 2021	Activity RecognitionClassification	—Unverified
A communication efficient distributed learning framework for smart environments	Sep 27, 2021	Activity Recognition	—Unverified
Incremental Learning Techniques for Online Human Activity Recognition	Sep 20, 2021	Activity RecognitionHuman Activity Recognition	—Unverified
Multiscale Manifold Warping	Sep 19, 2021	Activity RecognitionDynamic Time Warping	—Unverified
RAPID: Retrofitting IEEE 802.11ay Access Points for Indoor Human Detection and Sensing	Sep 10, 2021	Activity RecognitionHuman Activity Recognition	—Unverified
A distillation-based approach integrating continual learning and federated learning for pervasive services	Sep 9, 2021	Activity RecognitionContinual Learning	—Unverified
Sensor Data Augmentation by Resampling for Contrastive Learning in Human Activity Recognition	Sep 5, 2021	Activity RecognitionContrastive Learning	—Unverified
Few Shot Activity Recognition Using Variational Inference	Aug 20, 2021	Action RecognitionActivity 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