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 101–110 of 1322 papers

Title	Date	Tasks	Status	Hype
Self-supervised transfer learning of physiological representations from free-living wearable data	Nov 18, 2020	Activity RecognitionHuman Activity Recognition	CodeCode Available	1
Learning Generalizable Physiological Representations from Large-scale Wearable Data	Nov 9, 2020	Activity RecognitionHuman Activity Recognition	CodeCode Available	1
HHAR-net: Hierarchical Human Activity Recognition using Neural Networks	Oct 28, 2020	Activity RecognitionHuman Activity Recognition	CodeCode Available	1
Improved Actor Relation Graph based Group Activity Recognition	Oct 24, 2020	Activity RecognitionGroup Activity Recognition	CodeCode Available	1
Skeleton-based Action Recognition via Spatial and Temporal Transformer Networks	Aug 17, 2020	Action RecognitionAction Recognition In Videos	CodeCode Available	1
DANA: Dimension-Adaptive Neural Architecture for Multivariate Sensor Data	Aug 5, 2020	Activity RecognitionHuman Activity Recognition	CodeCode Available	1
SeCo: Exploring Sequence Supervision for Unsupervised Representation Learning	Aug 3, 2020	Action RecognitionActivity Recognition	CodeCode Available	1
3D Human Shape and Pose from a Single Low-Resolution Image with Self-Supervised Learning	Jul 27, 2020	3D Human Pose Estimation3D Shape Reconstruction	CodeCode Available	1
ESPRESSO: Entropy and ShaPe awaRe timE-Series SegmentatiOn for processing heterogeneous sensor data	Jul 24, 2020	Activity RecognitionChange Point Detection	CodeCode Available	1
Attention-Based Deep Learning Framework for Human Activity Recognition with User Adaptation	Jun 6, 2020	Activity RecognitionDeep Learning	CodeCode Available	1

Show:10 25 50

← PrevPage 11 of 133Next →

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