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 1311–1320 of 1322 papers

Title	Date	Tasks	Status
Kernel Learning for Extrinsic Classification of Manifold Features	Jun 1, 2013	Activity RecognitionClassification	—Unverified
HON4D: Histogram of Oriented 4D Normals for Activity Recognition from Depth Sequences	Jun 1, 2013	Activity Recognition	—Unverified
Spatio-temporal Depth Cuboid Similarity Feature for Activity Recognition Using Depth Camera	Jun 1, 2013	Activity Recognition	—Unverified
Recognize Human Activities from Partially Observed Videos	Jun 1, 2013	Activity PredictionActivity Recognition	—Unverified
Decoding Children's Social Behavior	Jun 1, 2013	Activity Recognition	—Unverified
First-Person Activity Recognition: What Are They Doing to Me?	Jun 1, 2013	Activity RecognitionGeneral Classification	—Unverified
Two-person interaction detection using body-pose features and multiple instance learning	Jul 16, 2012	Activity RecognitionHuman Activity Recognition	—Unverified
Probabilistic Event Calculus for Event Recognition	Jul 13, 2012	Activity Recognition	CodeCode Available
A Probabilistic Logic Programming Event Calculus	Apr 9, 2012	Activity RecognitionHuman Activity Recognition	CodeCode Available
Beyond Actions: Discriminative Models for Contextual Group Activities	Dec 1, 2010	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