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 981–990 of 1322 papers

Title	Date	Tasks	Status
Temporal Reasoning Graph for Activity Recognition	Aug 27, 2019	Action RecognitionActivity Recognition	—Unverified
Human activity recognition from skeleton poses	Aug 20, 2019	Action RecognitionActivity Recognition	CodeCode Available
Multi-Modal Recognition of Worker Activity for Human-Centered Intelligent Manufacturing	Aug 20, 2019	Activity PredictionActivity Recognition	—Unverified
Online Feature Selection for Activity Recognition using Reinforcement Learning with Multiple Feedback	Aug 16, 2019	Activity Recognitionfeature selection	—Unverified
Wi-Fringe: Leveraging Text Semantics in WiFi CSI-Based Device-Free Named Gesture Recognition	Aug 16, 2019	Activity RecognitionGesture Recognition	—Unverified
MEx: Multi-modal Exercises Dataset for Human Activity Recognition	Aug 13, 2019	Activity RecognitionHuman Activity Recognition	CodeCode Available
Three Branches: Detecting Actions With Richer Features	Aug 13, 2019	Action LocalizationActivity Recognition	—Unverified
Progressive Relation Learning for Group Activity Recognition	Aug 8, 2019	Activity RecognitionDeep Reinforcement Learning	—Unverified
Discriminating Spatial and Temporal Relevance in Deep Taylor Decompositions for Explainable Activity Recognition	Aug 5, 2019	Action RecognitionActivity Recognition	CodeCode Available
Mindful Active Learning	Jul 28, 2019	Active LearningActivity Recognition	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