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 541–550 of 1322 papers

Title	Date	Tasks	Status
A Deep Learning Framework using Passive WiFi Sensing for Respiration Monitoring	Apr 19, 2017	Activity RecognitionDeep Learning	—Unverified
Enhancing Human Action Recognition and Violence Detection Through Deep Learning Audiovisual Fusion	Aug 4, 2024	Action RecognitionActivity Recognition	—Unverified
Game Theory Solutions in Sensor-Based Human Activity Recognition: A Review	Nov 9, 2023	Activity RecognitionHuman Activity Recognition	—Unverified
Concurrent Activity Recognition with Multimodal CNN-LSTM Structure	Feb 6, 2017	Activity RecognitionConcurrent Activity Recognition	—Unverified
Application of Transfer Learning Approaches in Multimodal Wearable Human Activity Recognition	Jul 8, 2017	Activity RecognitionEnsemble Learning	—Unverified
Complex Activity Recognition using Granger Constrained DBN (GCDBN) in Sports and Surveillance Video	Jun 1, 2014	Activity RecognitionDescriptive	—Unverified
Application of Machine Learning Techniques in Human Activity Recognition	Oct 19, 2015	Action DetectionActivity Detection	—Unverified
A Deep Learning Approach To Multiple Kernel Fusion	Dec 28, 2016	Activity RecognitionDeep Learning	—Unverified
From Movements to Metrics: Evaluating Explainable AI Methods in Skeleton-Based Human Activity Recognition	Feb 20, 2024	Activity RecognitionHuman Activity Recognition	—Unverified
Comparative Analysis of XGBoost and Minirocket Algortihms for Human Activity Recognition	Feb 28, 2024	Activity RecognitionComputational Efficiency	—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