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 811–820 of 1322 papers

Title	Date	Tasks	Status
Human activity recognition based on time series analysis using U-Net	Sep 20, 2018	Activity RecognitionGesture Recognition	—Unverified
Human Activity Recognition for Edge Devices	Mar 18, 2019	Activity RecognitionHuman Activity Recognition	—Unverified
Human Activity Recognition for Mobile Robot	Jan 23, 2018	Activity RecognitionHuman Activity Recognition	—Unverified
Human activity recognition from mobile inertial sensors using recurrence plots	Dec 5, 2017	Activity RecognitionGeneral Classification	—Unverified
Human Activity Recognition from Wi-Fi CSI Data Using Principal Component-Based Wavelet CNN	Dec 26, 2022	Activity RecognitionHuman Activity Recognition	—Unverified
Human Activity Recognition in RGB-D Videos by Dynamic Images	Jul 9, 2018	Activity RecognitionHuman Activity Recognition	—Unverified
Human Activity Recognition Models in Ontology Networks	May 5, 2021	Activity RecognitionHuman Activity Recognition	—Unverified
Human Activity Recognition models using Limited Consumer Device Sensors and Machine Learning	Jan 21, 2022	Activity RecognitionBIG-bench Machine Learning	—Unverified
Human Activity Recognition on Microcontrollers with Quantized and Adaptive Deep Neural Networks	Sep 2, 2022	Activity RecognitionHuman Activity Recognition	—Unverified
Human Activity Recognition on Time Series Accelerometer Sensor Data using LSTM Recurrent Neural Networks	Jun 3, 2022	Activity RecognitionHuman 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