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 1221–1230 of 1322 papers

Title	Date	Tasks	Status
Ultra-compact Binary Neural Networks for Human Activity Recognition on RISC-V Processors	May 25, 2022	Activity RecognitionHuman Activity Recognition	CodeCode Available
Adaptive Client Selection with Personalization for Communication Efficient Federated Learning	Nov 26, 2024	Activity RecognitionFederated Learning	CodeCode Available
A benchmark of data stream classification for human activity recognition on connected objects	Aug 27, 2020	Activity RecognitionClassification	CodeCode Available
Activity2Vec: Learning ADL Embeddings from Sensor Data with a Sequence-to-Sequence Model	Jul 12, 2019	Activity RecognitionFeature Engineering	CodeCode Available
SelaFD:Seamless Adaptation of Vision Transformer Fine-tuning for Radar-based Human Activity	Feb 7, 2025	Activity RecognitionHuman Activity Recognition	CodeCode Available
Eidetic 3D LSTM: A Model for Video Prediction and Beyond	May 1, 2019	Activity RecognitionVideo Prediction	CodeCode Available
A Matter of Annotation: An Empirical Study on In Situ and Self-Recall Activity Annotations from Wearable Sensors	May 15, 2023	Activity RecognitionHuman Activity Recognition	CodeCode Available
Hybrid CNN-Dilated Self-attention Model Using Inertial and Body-Area Electrostatic Sensing for Gym Workout Recognition, Counting, and User Authentification	Mar 8, 2025	Activity Recognition	CodeCode Available
Transfer Learning for Activity Recognition in Mobile Health	Jul 12, 2020	Activity RecognitionTransfer Learning	CodeCode Available
Multimodal Explanations: Justifying Decisions and Pointing to the Evidence	Feb 15, 2018	Activity RecognitionExplainable Models	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