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 1001–1010 of 1322 papers

Title	Date	Tasks	Status
Model enhancement and personalization using weakly supervised learning for multi-modal mobile sensing	Oct 29, 2019	Activity RecognitionWeakly-supervised Learning	—Unverified
Enhancing Human Action Recognition and Violence Detection Through Deep Learning Audiovisual Fusion	Aug 4, 2024	Action RecognitionActivity Recognition	—Unverified
3D Human Activity Recognition with Reconfigurable Convolutional Neural Networks	Jan 26, 2015	Activity RecognitionHuman Activity Recognition	—Unverified
3D Human motion anticipation and classification	Dec 31, 2020	Action RecognitionActivity Recognition	—Unverified
A Causality-Aware Pattern Mining Scheme for Group Activity Recognition in a Pervasive Sensor Space	Dec 1, 2023	Activity RecognitionGroup Activity Recognition	—Unverified
Acceleration Estimation of Signal Propagation Path Length Changes for Wireless Sensing	Dec 30, 2023	Activity Recognitionparameter estimation	—Unverified
A Close Look into Human Activity Recognition Models using Deep Learning	Apr 26, 2022	Activity RecognitionDeep Learning	—Unverified
A communication efficient distributed learning framework for smart environments	Sep 27, 2021	Activity Recognition	—Unverified
A compact sequence encoding scheme for online human activity recognition in HRI applications	Dec 1, 2020	Action RecognitionActivity Recognition	—Unverified
A Comparative Study of Human Activity Recognition: Motion, Tactile, and multi-modal Approaches	May 13, 2025	Activity RecognitionClassification	—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