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 1251–1260 of 1322 papers

Title	Date	Tasks	Status
Action Recognition in Video Sequences using Deep Bi-Directional LSTM With CNN Features	Nov 28, 2017	Action RecognitionActivity Recognition	CodeCode Available
WSense: A Robust Feature Learning Module for Lightweight Human Activity Recognition	Mar 31, 2023	Activity RecognitionHuman Activity Recognition	CodeCode Available
The Contribution of Human Body Capacitance/Body-Area Electric Field To Individual and Collaborative Activity Recognition	Oct 26, 2022	Activity Recognition	CodeCode Available
Through-the-Wall Radar Human Activity Recognition WITHOUT Using Neural Networks	Jun 5, 2025	Activity RecognitionHuman Activity Recognition	CodeCode Available
Multivariate Human Activity Segmentation: Systematic Benchmark with ClaSP	Jan 1, 2025	Activity RecognitionChange Point Detection	CodeCode Available
Data Augmentation Techniques for Cross-Domain WiFi CSI-based Human Activity Recognition	Jan 1, 2024	Activity RecognitionData Augmentation	CodeCode Available
Approaches to human activity recognition via passive radar	Oct 31, 2024	Activity RecognitionHuman Activity Recognition	CodeCode Available
Multivariate Time Series Classification using Dilated Convolutional Neural Network	May 5, 2019	Activity RecognitionClassification	CodeCode Available
Weakly-guided Self-supervised Pretraining for Temporal Activity Detection	Nov 26, 2021	Action DetectionActivity Detection	CodeCode Available
B-HAR: an open-source baseline framework for in depth study of human activity recognition datasets and workflows	Jan 23, 2021	Activity RecognitionHuman Activity Recognition	CodeCode Available

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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