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 1141–1150 of 1322 papers

Title	Date	Tasks	Status	Hype
Time Series Segmentation through Automatic Feature Learning	Jan 16, 2018	Activity RecognitionEEG	—Unverified	0
Multivariate LSTM-FCNs for Time Series Classification	Jan 14, 2018	Action RecognitionActivity Recognition	CodeCode Available	1
Real-world Anomaly Detection in Surveillance Videos	Jan 12, 2018	Abnormal Event Detection In VideoActivity Recognition	CodeCode Available	1
Stratified Transfer Learning for Cross-domain Activity Recognition	Dec 25, 2017	Activity RecognitionCross-Domain Activity Recognition	—Unverified	0
Im2Flow: Motion Hallucination from Static Images for Action Recognition	Dec 12, 2017	Action RecognitionActivity Recognition	CodeCode Available	0
Human activity recognition from mobile inertial sensors using recurrence plots	Dec 5, 2017	Activity RecognitionGeneral Classification	—Unverified	0
Recognizing Plans by Learning Embeddings from Observed Action Distributions	Dec 5, 2017	Activity RecognitionWord Embeddings	—Unverified	0
Action Recognition in Video Sequences using Deep Bi-Directional LSTM With CNN Features	Nov 28, 2017	Action RecognitionActivity Recognition	CodeCode Available	0
Temporal Relational Reasoning in Videos	Nov 22, 2017	Action ClassificationAction Recognition	CodeCode Available	0
Fullie and Wiselie: A Dual-Stream Recurrent Convolutional Attention Model for Activity Recognition	Nov 21, 2017	Activity RecognitionHuman Activity Recognition	—Unverified	0

Show:10 25 50

← PrevPage 115 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