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 741–750 of 1322 papers

Title	Date	Tasks	Status
Process-aware Human Activity Recognition	Nov 13, 2024	Activity RecognitionHuman Activity Recognition	—Unverified
Process Optimization and Deployment for Sensor-Based Human Activity Recognition Based on Deep Learning	Mar 22, 2025	Activity RecognitionHuman Activity Recognition	—Unverified
Progress Estimation and Phase Detection for Sequential Processes	Feb 28, 2017	Activity RecognitionMultimodal Deep Learning	—Unverified
Progressive Relation Learning for Group Activity Recognition	Aug 8, 2019	Activity RecognitionDeep Reinforcement Learning	—Unverified
POND: Multi-Source Time Series Domain Adaptation with Information-Aware Prompt Tuning	Dec 19, 2023	Activity RecognitionDomain Adaptation	—Unverified
PromptGAR: Flexible Promptive Group Activity Recognition	Mar 11, 2025	Activity RecognitionDecoder	—Unverified
Provable Robustness for Streaming Models with a Sliding Window	Mar 28, 2023	Activity RecognitionHuman Activity Recognition	—Unverified
Provably Secure Federated Learning against Malicious Clients	Feb 3, 2021	Activity RecognitionFederated Learning	—Unverified
Pyramid Recurrent Neural Networks for Multi-Scale Change-Point Detection	May 1, 2019	Activity RecognitionChange Point Detection	—Unverified
Qiniu Submission to ActivityNet Challenge 2018	Jun 12, 2018	Activity RecognitionOptical Flow Estimation	—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