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 821–830 of 1322 papers

Title	Date	Tasks	Status
Sensor Data for Human Activity Recognition: Feature Representation and Benchmarking	May 15, 2020	Activity RecognitionBenchmarking	—Unverified
Sentence Directed Video Object Codetection	Jun 5, 2015	Activity RecognitionObject	—Unverified
Sequence Metric Learning as Synchronization of Recurrent Neural Networks	Jan 1, 2021	Activity RecognitionMetric Learning	—Unverified
Sequential Lifted Bayesian Filtering in Multiset Rewriting Systems	Jul 20, 2017	Activity RecognitionState Space Models	—Unverified
SETransformer: A Hybrid Attention-Based Architecture for Robust Human Activity Recognition	May 25, 2025	Activity RecognitionHuman Activity Recognition	—Unverified
SHADE-AD: An LLM-Based Framework for Synthesizing Activity Data of Alzheimer's Patients	Mar 3, 2025	Activity RecognitionHuman Activity Recognition	—Unverified
Shape Distributions of Nonlinear Dynamical Systems for Video-based Inference	Jan 27, 2016	Activity RecognitionGesture Recognition	—Unverified
Sharing Leaky-Integrate-and-Fire Neurons for Memory-Efficient Spiking Neural Networks	May 26, 2023	Activity RecognitionHuman Activity Recognition	—Unverified
Shopper Analytics: a customer activity recognition system using a distributed RGB-D camera network	Aug 27, 2015	Activity Recognition	—Unverified
Shuffled Differentially Private Federated Learning for Time Series Data Analytics	Jul 30, 2023	Activity RecognitionFederated Learning	—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