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 681–690 of 1322 papers

Title	Date	Tasks	Status
On Handling Catastrophic Forgetting for Incremental Learning of Human Physical Activity on the Edge	Feb 18, 2023	Activity RecognitionHuman Activity Recognition	—Unverified
Online Collective Animal Movement Activity Recognition	Nov 22, 2018	Activity Recognition	—Unverified
To Store or Not? Online Data Selection for Federated Learning with Limited Storage	Sep 1, 2022	Activity RecognitionData Valuation	—Unverified
Online Feature Selection for Activity Recognition using Reinforcement Learning with Multiple Feedback	Aug 16, 2019	Activity Recognitionfeature selection	—Unverified
Online Guest Detection in a Smart Home using Pervasive Sensors and Probabilistic Reasoning	Mar 13, 2020	Activity RecognitionCrowd Counting	—Unverified
Online Human Activity Recognition Employing Hierarchical Hidden Markov Models	Mar 12, 2019	Activity RecognitionHuman Activity Recognition	—Unverified
Online Human Activity Recognition using Low-Power Wearable Devices	Aug 26, 2018	Activity RecognitionHuman Activity Recognition	—Unverified
On Matched Filtering for Statistical Change Point Detection	Jun 9, 2020	Activity RecognitionChange Point Detection	—Unverified
On Multi-resident Activity Recognition in Ambient Smart-Homes	Jun 18, 2018	Activity Recognition	—Unverified
On Neural Inertial Classification Networks for Pedestrian Activity Recognition	Feb 23, 2025	Activity RecognitionBenchmarking	—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