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 1111–1120 of 1322 papers

Title	Date	Tasks	Status
On Multi-resident Activity Recognition in Ambient Smart-Homes	Jun 18, 2018	Activity Recognition	—Unverified
Object Level Visual Reasoning in Videos	Jun 16, 2018	Activity RecognitionHuman Activity Recognition	CodeCode Available
Human Activity Recognition Based on Wearable Sensor Data: A Standardization of the State-of-the-Art	Jun 13, 2018	Activity RecognitionHuman Activity Recognition	CodeCode Available
Qiniu Submission to ActivityNet Challenge 2018	Jun 12, 2018	Activity RecognitionOptical Flow Estimation	—Unverified
Egocentric Activity Recognition on a Budget	Jun 1, 2018	Activity RecognitionEgocentric Activity Recognition	—Unverified
Visually Guided Spatial Relation Extraction from Text	Jun 1, 2018	Activity RecognitionImage Captioning	—Unverified
Surgical Activity Recognition in Robot-Assisted Radical Prostatectomy using Deep Learning	Jun 1, 2018	Activity Recognition	—Unverified
STS Classification with Dual-stream CNN	May 20, 2018	Activity RecognitionClassification	—Unverified
On Attention Models for Human Activity Recognition	May 19, 2018	Activity RecognitionHuman Activity Recognition	—Unverified
Understanding and Improving Deep Neural Network for Activity Recognition	May 18, 2018	Activity RecognitionGeneral Classification	CodeCode Available

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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