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 1281–1290 of 1322 papers

Title	Date	Tasks	Status
Pooled Motion Features for First-Person Videos	Dec 19, 2014	Activity RecognitionActivity Recognition In Videos	CodeCode Available
Multiple object tracking with context awareness	Nov 24, 2014	Activity RecognitionMultiple Object Tracking	—Unverified
Dynamic Programming for Instance Annotation in Multi-instance Multi-label Learning	Nov 14, 2014	Activity RecognitionMulti-Label Learning	—Unverified
The Evolution of First Person Vision Methods: A Survey	Sep 4, 2014	Activity Recognitionobject-detection	—Unverified
Boosted Markov Networks for Activity Recognition	Aug 6, 2014	Activity Recognitionfeature selection	—Unverified
See No Evil, Say No Evil: Description Generation from Densely Labeled Images	Aug 1, 2014	Activity RecognitionObject Recognition	—Unverified
Analysis of Gait Pattern to Recognize the Human Activities	Jul 18, 2014	Activity RecognitionHuman Activity Recognition	—Unverified
Fine-grained Activity Recognition with Holistic and Pose based Features	Jun 7, 2014	Activity Recognition	—Unverified
Incremental Activity Modeling and Recognition in Streaming Videos	Jun 1, 2014	Active LearningActivity Recognition	—Unverified
Complex Activity Recognition using Granger Constrained DBN (GCDBN) in Sports and Surveillance Video	Jun 1, 2014	Activity RecognitionDescriptive	—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