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 1101–1110 of 1322 papers

Title	Date	Tasks	Status
Can You Spot the Semantic Predicate in this Video?	Aug 1, 2018	Activity RecognitionHuman Activity Recognition	—Unverified
DFTerNet: Towards 2-bit Dynamic Fusion Networks for Accurate Human Activity Recognition	Jul 31, 2018	Action DetectionActivity Detection	—Unverified
Attention is All We Need: Nailing Down Object-centric Attention for Egocentric Activity Recognition	Jul 31, 2018	Activity RecognitionAll	CodeCode Available
Deep Transfer Learning for Cross-domain Activity Recognition	Jul 20, 2018	Activity RecognitionCross-Domain Activity Recognition	—Unverified
Human Activity Recognition in RGB-D Videos by Dynamic Images	Jul 9, 2018	Activity RecognitionHuman Activity Recognition	—Unverified
A Survey of Knowledge Representation in Service Robotics	Jul 5, 2018	Activity RecognitionBIG-bench Machine Learning	—Unverified
Who did What at Where and When: Simultaneous Multi-Person Tracking and Activity Recognition	Jul 3, 2018	Activity RecognitionVisual Tracking	—Unverified
Multi-modal Egocentric Activity Recognition using Audio-Visual Features	Jul 2, 2018	Activity RecognitionEgocentric Activity Recognition	—Unverified
Human Activity Prediction in Smart Home Environments with LSTM Neural Networks	Jun 28, 2018	Activity PredictionActivity Recognition	—Unverified
Cross-position Activity Recognition with Stratified Transfer Learning	Jun 26, 2018	Activity RecognitionHuman Activity Recognition	—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