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 251–260 of 1322 papers

Title	Date	Tasks	Status	Score
Adversarial Attacks on Deep Neural Networks for Time Series Classification	Mar 17, 2019	Activity RecognitionAdversarial Attack	CodeCode Available	5
Chirality Nets for Human Pose Regression	Oct 31, 2019	3D Human Pose Estimation3D Pose Estimation	CodeCode Available	5
DYSAN: Dynamically sanitizing motion sensor data against sensitive inferences through adversarial networks	Mar 23, 2020	Activity RecognitionAttribute	CodeCode Available	5
Choose Your Explanation: A Comparison of SHAP and GradCAM in Human Activity Recognition	Dec 20, 2024	Activity RecognitionFeature Importance	CodeCode Available	5
Enhancing Wearable Tap Water Audio Detection through Subclass Annotation in the HD-Epic Dataset	May 27, 2025	Activity RecognitionHuman Activity Recognition	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
Does SpatioTemporal information benefit Two video summarization benchmarks?	Oct 4, 2024	Activity RecognitionVideo Summarization	CodeCode Available	5
Domain Adaptation with Representation Learning and Nonlinear Relation for Time Series	Feb 15, 2022	Activity RecognitionDomain Adaptation	CodeCode Available	5
Distributed Online Learning of Event Definitions	May 5, 2017	Activity RecognitionForm	CodeCode Available	5
Action Recognition in Video Sequences using Deep Bi-Directional LSTM With CNN Features	Nov 28, 2017	Action RecognitionActivity Recognition	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