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 701–710 of 1322 papers

Title	Date	Tasks	Status	Hype
A communication efficient distributed learning framework for smart environments	Sep 27, 2021	Activity Recognition	—Unverified	0
Incremental Learning Techniques for Online Human Activity Recognition	Sep 20, 2021	Activity RecognitionHuman Activity Recognition	—Unverified	0
Multiscale Manifold Warping	Sep 19, 2021	Activity RecognitionDynamic Time Warping	—Unverified	0
RAPID: Retrofitting IEEE 802.11ay Access Points for Indoor Human Detection and Sensing	Sep 10, 2021	Activity RecognitionHuman Activity Recognition	—Unverified	0
A distillation-based approach integrating continual learning and federated learning for pervasive services	Sep 9, 2021	Activity RecognitionContinual Learning	—Unverified	0
Sensor Data Augmentation by Resampling for Contrastive Learning in Human Activity Recognition	Sep 5, 2021	Activity RecognitionContrastive Learning	—Unverified	0
Transformer Networks for Data Augmentation of Human Physical Activity Recognition	Sep 2, 2021	Activity RecognitionData Augmentation	CodeCode Available	1
GroupFormer: Group Activity Recognition with Clustered Spatial-Temporal Transformer	Aug 28, 2021	Activity RecognitionGroup Activity Recognition	CodeCode Available	1
Spatio-Temporal Dynamic Inference Network for Group Activity Recognition	Aug 26, 2021	Activity RecognitionGroup Activity Recognition	CodeCode Available	1
Few Shot Activity Recognition Using Variational Inference	Aug 20, 2021	Action RecognitionActivity Recognition	—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