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 1191–1200 of 1322 papers

Title	Date	Tasks	Status
CHARM: A Hierarchical Deep Learning Model for Classification of Complex Human Activities Using Motion Sensors	Jul 16, 2022	Activity RecognitionHuman Activity Recognition	—Unverified
Cheating off your neighbors: Improving activity recognition through corroboration	May 27, 2023	Activity RecognitionHuman Activity Recognition	—Unverified
CKSP: Cross-species Knowledge Sharing and Preserving for Universal Animal Activity Recognition	Oct 22, 2024	Activity Recognition	—Unverified
CLAD: A Complex and Long Activities Dataset with Rich Crowdsourced Annotations	Sep 11, 2017	Activity Recognitionobject-detection	—Unverified
Self-supervised New Activity Detection in Sensor-based Smart Environments	Jan 17, 2024	Action DetectionActivity Detection	—Unverified
Classification of grasping tasks based on EEG-EMG coherence	Sep 10, 2018	Activity RecognitionClassification	—Unverified
Classification of human activity recognition using smartphones	Jan 6, 2020	Activity RecognitionClassification	—Unverified
Classifying Human Activities using Machine Learning and Deep Learning Techniques	May 19, 2022	Activity RecognitionBIG-bench Machine Learning	—Unverified
Classifying Human Activities with Inertial Sensors: A Machine Learning Approach	Nov 9, 2021	Activity RecognitionBIG-bench Machine Learning	—Unverified
CMD-HAR: Cross-Modal Disentanglement for Wearable Human Activity Recognition	Mar 27, 2025	Activity RecognitionDisentanglement	—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