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 1071–1080 of 1322 papers

Title	Date	Tasks	Status	Hype
Distributionally Robust Semi-Supervised Learning for People-Centric Sensing	Nov 12, 2018	Activity RecognitionGesture Recognition	—Unverified	0
BAR: Bayesian Activity Recognition using variational inference	Nov 8, 2018	Activity RecognitionBayesian Inference	—Unverified	0
PerceptionNet: A Deep Convolutional Neural Network for Late Sensor Fusion	Nov 1, 2018	Activity RecognitionHuman Activity Recognition	—Unverified	0
Skeleton-based Activity Recognition with Local Order Preserving Match of Linear Patches	Nov 1, 2018	Activity RecognitionHuman Activity Recognition	—Unverified	0
Informed Democracy: Voting-based Novelty Detection for Action Recognition	Oct 30, 2018	Action ClassificationAction Recognition	—Unverified	0
Wi-Motion: A Robust Human Activity Recognition Using WiFi Signals	Oct 27, 2018	Activity RecognitionHuman Activity Recognition	—Unverified	0
Mobile Sensor Data Anonymization	Oct 26, 2018	Activity RecognitionDecoder	CodeCode Available	1
A Preliminary Study on Hyperparameter Configuration for Human Activity Recognition	Oct 25, 2018	Activity RecognitionClassification	—Unverified	0
HAR-Net:Fusing Deep Representation and Hand-crafted Features for Human Activity Recognition	Oct 25, 2018	Activity RecognitionDeep Learning	—Unverified	0
Audio-Based Activities of Daily Living (ADL) Recognition with Large-Scale Acoustic Embeddings from Online Videos	Oct 19, 2018	Activity Recognition	CodeCode Available	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