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 1201–1210 of 1322 papers

Title	Date	Tasks	Status
Human Activity Recognition using Continuous Wavelet Transform and Convolutional Neural Networks	Jun 23, 2021	Activity RecognitionHuman Activity Recognition	CodeCode Available
SBGAR: Semantics Based Group Activity Recognition	Oct 1, 2017	Activity RecognitionGroup Activity Recognition	CodeCode Available
PI-Net: A Deep Learning Approach to Extract Topological Persistence Images	Jun 5, 2019	Activity RecognitionDeep Learning	CodeCode Available
M-PACT: An Open Source Platform for Repeatable Activity Classification Research	Apr 16, 2018	Action ClassificationActivity Recognition	CodeCode Available
ActiveHARNet: Towards On-Device Deep Bayesian Active Learning for Human Activity Recognition	May 31, 2019	Active LearningActivity Recognition	CodeCode Available
Pooled Motion Features for First-Person Videos	Dec 19, 2014	Activity RecognitionActivity Recognition In Videos	CodeCode Available
Mukhtasir-Khail-Net: An Ultra-Efficient Convolutional Neural Network for Sports Activity Recognition with Wearable Inertial Sensors	Oct 22, 2024	Activity RecognitionHuman Activity Recognition	CodeCode Available
Human Activity Recognition using Multi-Head CNN followed by LSTM	Feb 21, 2020	Activity RecognitionBIG-bench Machine Learning	CodeCode Available
TxP: Reciprocal Generation of Ground Pressure Dynamics and Activity Descriptions for Improving Human Activity Recognition	May 4, 2025	Activity RecognitionData Augmentation	CodeCode Available
Enhancing Wearable Tap Water Audio Detection through Subclass Annotation in the HD-Epic Dataset	May 27, 2025	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