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 601–610 of 1322 papers

Title	Date	Tasks	Status
A Survey of IMU Based Cross-Modal Transfer Learning in Human Activity Recognition	Mar 17, 2024	Action RecognitionActivity Recognition	—Unverified
Decoding Human Activities: Analyzing Wearable Accelerometer and Gyroscope Data for Activity Recognition	Oct 3, 2023	Activity RecognitionHuman Activity Recognition	—Unverified
A Fourier Domain Feature Approach for Human Activity Recognition & Fall Detection	Mar 11, 2020	Activity RecognitionData Augmentation	—Unverified
Decoding Children's Social Behavior	Jun 1, 2013	Activity Recognition	—Unverified
Day2Dark: Pseudo-Supervised Activity Recognition beyond Silent Daylight	Dec 5, 2022	Activity RecognitionDomain Adaptation	—Unverified
A Survey of Human Activity Recognition in Smart Homes Based on IoT Sensors Algorithms: Taxonomies, Challenges, and Opportunities with Deep Learning	Oct 18, 2021	Activity RecognitionHuman Activity Recognition	—Unverified
Dataset Bias in Human Activity Recognition	Jan 19, 2023	Activity RecognitionHuman Activity Recognition	—Unverified
A Survey of Application of Machine Learning in Wireless Indoor Positioning Systems	Mar 7, 2024	Activity RecognitionHuman Detection	—Unverified
Affinity-Based Hierarchical Learning of Dependent Concepts for Human Activity Recognition	Apr 11, 2021	Activity RecognitionGeneral Classification	—Unverified
Dataiku's Solution to SPHERE's Activity Recognition Challenge	Oct 10, 2016	Activity RecognitionBIG-bench Machine Learning	—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