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 721–730 of 1322 papers

Title	Date	Tasks	Status
Unsupervised Doppler Radar-Based Activity Recognition for e-Healthcare	Mar 18, 2021	Activity RecognitionTexture Classification	—Unverified
Unsupervised Embedding Learning for Human Activity Recognition Using Wearable Sensor Data	Jul 21, 2023	Activity RecognitionClustering	—Unverified
Unsupervised explainable activity prediction in competitive Nordic Walking from experimental data	Jun 18, 2024	Activity PredictionActivity Recognition	—Unverified
Unsupervised Human Action Detection by Action Matching	Dec 2, 2016	Action DetectionActivity Recognition	—Unverified
Unsupervised Learning of 3D Scene Flow with 3D Odometry Assistance	Sep 11, 2022	Activity RecognitionAutonomous Driving	—Unverified
Unsupervised Segmentation of Action Segments in Egocentric Videos using Gaze	Sep 30, 2017	Activity RecognitionRetrieval	—Unverified
Unsupervised Statistical Feature-Guided Diffusion Model for Sensor-based Human Activity Recognition	May 30, 2023	Activity RecognitionGenerative Adversarial Network	—Unverified
Unsupervised Synthesis of Anomalies in Videos: Transforming the Normal	Apr 14, 2019	Activity RecognitionData Augmentation	—Unverified
Unsupervised Video Anomaly Detection for Stereotypical Behaviours in Autism	Feb 27, 2023	Activity RecognitionAnomaly Detection	—Unverified
Using GAN to Enhance the Accuracy of Indoor Human Activity Recognition	Apr 23, 2020	Activity RecognitionGenerative Adversarial Network	—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