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 831–840 of 1322 papers

Title	Date	Tasks	Status
Human Activity Recognition using Recurrent Neural Networks	Apr 19, 2018	Activity RecognitionBIG-bench Machine Learning	—Unverified
Human Activity Recognition Using Robust Adaptive Privileged Probabilistic Learning	Sep 19, 2017	Activity RecognitionHuman Activity Recognition	—Unverified
Human Activity Recognition Using Self-Supervised Representations of Wearable Data	Apr 26, 2023	Activity RecognitionHuman Activity Recognition	—Unverified
Human Activity Recognition using Smartphone	Jan 30, 2014	Active LearningActivity Recognition	—Unverified
Human Activity Recognition using Smartphones	Apr 3, 2024	Activity RecognitionDimensionality Reduction	—Unverified
Human Activity Recognition Using Tools of Convolutional Neural Networks: A State of the Art Review, Data Sets, Challenges and Future Prospects	Feb 2, 2022	Activity RecognitionHuman Activity Recognition	—Unverified
Human Activity Recognition with a 6.5 GHz Reconfigurable Intelligent Surface for Wi-Fi 6E	Jan 24, 2025	Activity RecognitionGesture Recognition	—Unverified
Human Activity Recognition with Low-Resolution Infrared Array Sensor Using Semi-supervised Cross-domain Neural Networks for Indoor Environment	Mar 5, 2024	Activity RecognitionDomain Adaptation	—Unverified
Human Body Parts Tracking: Applications to Activity Recognition	Jul 2, 2019	Activity RecognitionHuman Activity Recognition	—Unverified
Human Interaction Learning on 3D Skeleton Point Clouds for Video Violence Recognition	Aug 1, 2020	Activity Recognition	—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