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 851–860 of 1322 papers

Title	Date	Tasks	Status	Hype
Self-Supervised Human Activity Recognition by Augmenting Generative Adversarial Networks	Aug 26, 2020	Activity RecognitionGenerative Adversarial Network	—Unverified	0
Drive Safe: Cognitive-Behavioral Mining for Intelligent Transportation Cyber-Physical System	Aug 24, 2020	Activity RecognitionSequential Pattern Mining	—Unverified	0
AssembleNet++: Assembling Modality Representations via Attention Connections	Aug 18, 2020	Action ClassificationActivity Recognition	CodeCode Available	0
Skeleton-based Action Recognition via Spatial and Temporal Transformer Networks	Aug 17, 2020	Action RecognitionAction Recognition In Videos	CodeCode Available	1
An Intelligent Non-Invasive Real Time Human Activity Recognition System for Next-Generation Healthcare	Aug 6, 2020	Activity RecognitionBIG-bench Machine Learning	—Unverified	0
DANA: Dimension-Adaptive Neural Architecture for Multivariate Sensor Data	Aug 5, 2020	Activity RecognitionHuman Activity Recognition	CodeCode Available	1
SpinAPS: A High-Performance Spintronic Accelerator for Probabilistic Spiking Neural Networks	Aug 5, 2020	Activity RecognitionHuman Activity Recognition	—Unverified	0
A Novel Indoor Positioning System for unprepared firefighting scenarios	Aug 4, 2020	Activity RecognitionOptical Flow Estimation	—Unverified	0
HAMLET: A Hierarchical Multimodal Attention-based Human Activity Recognition Algorithm	Aug 3, 2020	Activity RecognitionHuman Activity Recognition	—Unverified	0
SeCo: Exploring Sequence Supervision for Unsupervised Representation Learning	Aug 3, 2020	Action RecognitionActivity Recognition	CodeCode Available	1

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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