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 111–120 of 1322 papers

Title	Date	Tasks	Status	Hype
SleepPoseNet: Multi-View Learning for Sleep Postural Transition Recognition Using UWB	May 2, 2020	Activity RecognitionData Augmentation	CodeCode Available	1
Human Activity Recognition from Wearable Sensor Data Using Self-Attention	Mar 17, 2020	Activity RecognitionHuman Activity Recognition	CodeCode Available	1
Gimme Signals: Discriminative signal encoding for multimodal activity recognition	Mar 13, 2020	Action RecognitionActivity Recognition	CodeCode Available	1
Convolutional Tensor-Train LSTM for Spatio-temporal Learning	Feb 21, 2020	Activity RecognitionVideo Compression	CodeCode Available	1
Privacy and Utility Preserving Sensor-Data Transformations	Nov 14, 2019	Activity Recognition	CodeCode Available	1
NTU RGB+D 120: A Large-Scale Benchmark for 3D Human Activity Understanding	May 12, 2019	Action RecognitionActivity Recognition	CodeCode Available	1
Mobile Sensor Data Anonymization	Oct 26, 2018	Activity RecognitionDecoder	CodeCode Available	1
Semi-Supervised Online Structure Learning for Composite Event Recognition	Mar 1, 2018	Activity RecognitionHuman Activity Recognition	CodeCode Available	1
Protecting Sensory Data against Sensitive Inferences	Feb 21, 2018	Activity RecognitionAttribute	CodeCode Available	1
Multivariate LSTM-FCNs for Time Series Classification	Jan 14, 2018	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