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

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Showing 331–340 of 1322 papers

Title	Date	Tasks	Status
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
A Survey of IMU Based Cross-Modal Transfer Learning in Human Activity Recognition	Mar 17, 2024	Action RecognitionActivity Recognition	—Unverified
Decoupled Prompt-Adapter Tuning for Continual Activity Recognition	Jul 20, 2024	Action RecognitionActivity Recognition	—Unverified
Deep Action- and Context-Aware Sequence Learning for Activity Recognition and Anticipation	Nov 17, 2016	Action RecognitionActivity Recognition	—Unverified
Deep Activity Recognition Models with Triaxial Accelerometers	Nov 15, 2015	Activity RecognitionHuman Activity Recognition	—Unverified
Deep Adaptive Temporal Pooling for Activity Recognition	Aug 22, 2018	Activity RecognitionHuman Activity Recognition	—Unverified
Deep Adversarial Learning with Activity-Based User Discrimination Task for Human Activity Recognition	Oct 1, 2024	Activity RecognitionHuman Activity Recognition	—Unverified
Deep Auto-Set: A Deep Auto-Encoder-Set Network for Activity Recognition Using Wearables	Nov 20, 2018	Activity RecognitionMulti-class Classification	—Unverified
CROMOSim: A Deep Learning-based Cross-modality Inertial Measurement Simulator	Feb 21, 2022	Activity RecognitionData Augmentation	—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