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 1251–1260 of 1322 papers

Title	Date	Tasks	Status
Improving Human Activity Recognition Through Ranking and Re-ranking	Dec 11, 2015	Activity RecognitionHuman Activity Recognition	—Unverified
Gated networks: an inventory	Dec 10, 2015	Activity Recognition	—Unverified
A Deep Structured Model with Radius-Margin Bound for 3D Human Activity Recognition	Dec 5, 2015	Activity RecognitionHuman Activity Recognition	—Unverified
Context Aware Active Learning of Activity Recognition Models	Dec 1, 2015	Active LearningActivity Recognition	—Unverified
Learning Ensembles of Potential Functions for Structured Prediction With Latent Variables	Dec 1, 2015	Action RecognitionActivity Recognition	—Unverified
ActionNet-VE Dataset: A Dataset for Describing Visual Events by Extending VIRAT Ground 2.0	Nov 25, 2015	Activity RecognitionHuman Activity Recognition	—Unverified
A Hierarchical Deep Temporal Model for Group Activity Recognition	Nov 19, 2015	Activity RecognitionGroup Activity Recognition	CodeCode Available
Deep Activity Recognition Models with Triaxial Accelerometers	Nov 15, 2015	Activity RecognitionHuman Activity Recognition	—Unverified
Structure Inference Machines: Recurrent Neural Networks for Analyzing Relations in Group Activity Recognition	Nov 13, 2015	Activity RecognitionGroup Activity Recognition	—Unverified
Application of Machine Learning Techniques in Human Activity Recognition	Oct 19, 2015	Action DetectionActivity Detection	—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