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 211–220 of 1322 papers

Title	Date	Tasks	Status	Score
Eidetic 3D LSTM: A Model for Video Prediction and Beyond	May 1, 2019	Activity RecognitionVideo Prediction	CodeCode Available	5
Domain Adaptation with Representation Learning and Nonlinear Relation for Time Series	Feb 15, 2022	Activity RecognitionDomain Adaptation	CodeCode Available	5
Batch-based Activity Recognition from Egocentric Photo-Streams Revisited	Oct 11, 2017	Activity Recognition	CodeCode Available	5
Large-scale weakly-supervised pre-training for video action recognition	May 2, 2019	Action ClassificationAction Recognition	CodeCode Available	5
Through-the-Wall Radar Human Activity Recognition WITHOUT Using Neural Networks	Jun 5, 2025	Activity RecognitionHuman Activity Recognition	CodeCode Available	5
Activity2Vec: Learning ADL Embeddings from Sensor Data with a Sequence-to-Sequence Model	Jul 12, 2019	Activity RecognitionFeature Engineering	CodeCode Available	5
Learning Latent Sub-events in Activity Videos Using Temporal Attention Filters	May 26, 2016	Action ClassificationAction Recognition In Videos	CodeCode Available	5
Domain Adaptation Under Behavioral and Temporal Shifts for Natural Time Series Mobile Activity Recognition	Jul 10, 2022	Activity RecognitionContrastive Learning	CodeCode Available	5
Dynamic Vision Sensors for Human Activity Recognition	Mar 13, 2018	Activity RecognitionHuman Activity Recognition	CodeCode Available	5
Attention-Refined Unrolling for Sparse Sequential micro-Doppler Reconstruction	Jun 25, 2023	Activity Recognitioncompressed sensing	CodeCode Available	5

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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