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 431–440 of 1322 papers

Title	Date	Tasks	Status
Benchmarking Classical, Deep, and Generative Models for Human Activity Recognition	Jan 14, 2025	Activity RecognitionBenchmarking	—Unverified
Eco-Friendly Sensing for Human Activity Recognition	Jul 30, 2023	Activity RecognitionHuman Activity Recognition	—Unverified
EdgeServe: A Streaming System for Decentralized Model Serving	Mar 2, 2023	Activity RecognitionAutonomous Driving	—Unverified
Effective Human Activity Recognition Based on Small Datasets	Apr 29, 2020	Activity RecognitionHuman Activity Recognition	—Unverified
Layer-wise training convolutional neural networks with smaller filters for human activity recognition using wearable sensors	May 8, 2020	Activity RecognitionHuman Activity Recognition	—Unverified
Efficient data-driven encoding of scene motion using Eccentricity	Mar 3, 2021	Activity RecognitionIntent Recognition	—Unverified
EfficientFi: Towards Large-Scale Lightweight WiFi Sensing via CSI Compression	Apr 8, 2022	Activity RecognitionCloud Computing	—Unverified
Efficient Multi-stream Temporal Learning and Post-fusion Strategy for 3D Skeleton-based Hand Activity Recognition	Feb 10, 2021	Activity RecognitionSkeleton Based Action Recognition	—Unverified
Efficient Retail Video Annotation: A Robust Key Frame Generation Approach for Product and Customer Interaction Analysis	Jun 17, 2025	Activity Recognition	—Unverified
A Real-time Human Pose Estimation Approach for Optimal Sensor Placement in Sensor-based Human Activity Recognition	Jul 6, 2023	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