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 621–630 of 1322 papers

Title	Date	Tasks	Status
Cross-modal Learning for Multi-modal Video Categorization	Mar 7, 2020	Activity Recognitionobject-detection	—Unverified
Homogenization of Existing Inertial-Based Datasets to Support Human Activity Recognition	Jan 17, 2022	Activity RecognitionHuman Activity Recognition	—Unverified
Follow the Attention: Combining Partial Pose and Object Motion for Fine-Grained Action Detection	May 11, 2019	Action DetectionActivity Detection	—Unverified
An Unsupervised Approach for Automatic Activity Recognition based on Hidden Markov Model Regression	Dec 25, 2013	Activity RecognitionGeneral Classification	—Unverified
Human Activity Analysis and Recognition from Smartphones using Machine Learning Techniques	Mar 30, 2021	Activity RecognitionBIG-bench Machine Learning	—Unverified
Human Activity Behavioural Pattern Recognition in Smarthome with Long-hour Data Collection	Jun 23, 2023	Activity RecognitionHuman Activity Recognition	—Unverified
Human Activity Prediction in Smart Home Environments with LSTM Neural Networks	Jun 28, 2018	Activity PredictionActivity Recognition	—Unverified
Cross-Subject Transfer Learning in Human Activity Recognition Systems using Generative Adversarial Networks	Mar 29, 2019	Activity RecognitionGenerative Adversarial Network	—Unverified
Human Activity Recognition based on Dynamic Spatio-Temporal Relations	Jun 29, 2020	Activity RecognitionHuman Activity Recognition	—Unverified
ActionNet-VE Dataset: A Dataset for Describing Visual Events by Extending VIRAT Ground 2.0	Nov 25, 2015	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