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 631–640 of 1322 papers

Title	Date	Tasks	Status
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
A Semi-supervised Approach for Activity Recognition from Indoor Trajectory Data	Jan 9, 2023	Activity RecognitionDecision Making	—Unverified
Human activity recognition from mobile inertial sensors using recurrence plots	Dec 5, 2017	Activity RecognitionGeneral Classification	—Unverified
Human Activity Behavioural Pattern Recognition in Smarthome with Long-hour Data Collection	Jun 23, 2023	Activity RecognitionHuman Activity Recognition	—Unverified
Human Activity Analysis and Recognition from Smartphones using Machine Learning Techniques	Mar 30, 2021	Activity RecognitionBIG-bench Machine Learning	—Unverified
Human Activity Recognition from Wi-Fi CSI Data Using Principal Component-Based Wavelet CNN	Dec 26, 2022	Activity RecognitionHuman Activity Recognition	—Unverified
Cross-position Activity Recognition with Stratified Transfer Learning	Jun 26, 2018	Activity RecognitionHuman Activity Recognition	—Unverified
Human Action Attribute Learning From Video Data Using Low-Rank Representations	Dec 23, 2016	Action RecognitionActivity Recognition	—Unverified
HON4D: Histogram of Oriented 4D Normals for Activity Recognition from Depth Sequences	Jun 1, 2013	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