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 491–500 of 1322 papers

Title	Date	Tasks	Status
Exploring the Capabilities of LLMs for IMU-based Fine-grained Human Activity Understanding	Apr 2, 2025	Activity RecognitionFew-Shot Learning	—Unverified
Exploring the Impact of Synthetic Data on Human Gesture Recognition Tasks Using GANs	Dec 9, 2024	Activity RecognitionGesture Recognition	—Unverified
Exploring Transformers for On-Line Handwritten Signature Verification	Jul 4, 2023	Activity Recognition	—Unverified
Maximum Likelihood Speed Estimation of Moving Objects in Video Signals	Mar 10, 2020	Activity RecognitionHuman Activity Recognition	—Unverified
Extreme Low Resolution Activity Recognition with Multi-Siamese Embedding Learning	Aug 3, 2017	Activity RecognitionPrivacy Preserving	—Unverified
Extreme Low Resolution Activity Recognition with Confident Spatial-Temporal Attention Transfer	Sep 9, 2019	Activity RecognitionPrivacy Preserving	—Unverified
Distributed Agent-Based Collaborative Learning in Cross-Individual Wearable Sensor-Based Human Activity Recognition	Nov 6, 2023	Activity RecognitionHuman Activity Recognition	—Unverified
Fast Low-parameter Video Activity Localization in Collaborative Learning Environments	Mar 2, 2024	Action DetectionActivity Detection	—Unverified
Distilled Mid-Fusion Transformer Networks for Multi-Modal Human Activity Recognition	May 5, 2023	Activity RecognitionFeature Engineering	—Unverified
Automated Human Activity Recognition by Colliding Bodies Optimization-based Optimal Feature Selection with Recurrent Neural Network	Oct 7, 2020	Activity Recognitionfeature selection	—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