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 1191–1200 of 1322 papers

Title	Date	Tasks	Status
Identifying First-person Camera Wearers in Third-person Videos	Apr 20, 2017	Activity RecognitionObject Tracking	—Unverified
A Deep Learning Framework using Passive WiFi Sensing for Respiration Monitoring	Apr 19, 2017	Activity RecognitionDeep Learning	—Unverified
Interpretable 3D Human Action Analysis with Temporal Convolutional Networks	Apr 14, 2017	3D Action RecognitionAction Analysis	CodeCode Available
Recognizing Activities of Daily Living from Egocentric Images	Apr 13, 2017	Activity RecognitionGeneral Classification	—Unverified
CERN: Confidence-Energy Recurrent Network for Group Activity Recognition	Apr 10, 2017	Activity RecognitionGroup Activity Recognition	—Unverified
Generalized Rank Pooling for Activity Recognition	Apr 7, 2017	Action RecognitionActivity Recognition	—Unverified
TS-LSTM and Temporal-Inception: Exploiting Spatiotemporal Dynamics for Activity Recognition	Mar 30, 2017	Action ClassificationAction Recognition	CodeCode Available
Pose-conditioned Spatio-Temporal Attention for Human Action Recognition	Mar 29, 2017	Action RecognitionActivity Recognition	—Unverified
Ensembles of Deep LSTM Learners for Activity Recognition using Wearables	Mar 28, 2017	Activity RecognitionHuman Activity Recognition	—Unverified
Progress Estimation and Phase Detection for Sequential Processes	Feb 28, 2017	Activity RecognitionMultimodal Deep Learning	—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