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 121–130 of 1322 papers

Title	Date	Tasks	Status	Hype
Understanding the Vulnerability of Skeleton-based Human Activity Recognition via Black-box Attack	Nov 21, 2022	Activity RecognitionAdversarial Attack	CodeCode Available	1
Improved Actor Relation Graph based Group Activity Recognition	Oct 24, 2020	Activity RecognitionGroup Activity Recognition	CodeCode Available	1
Multi-stage Learning for Radar Pulse Activity Segmentation	Dec 15, 2023	Activity RecognitionSegmentation	CodeCode Available	1
Anomaly Recognition from surveillance videos using 3D Convolutional Neural Networks	Jan 4, 2021	Activity RecognitionAnomaly Detection	—Unverified	0
Anomaly detection and regime searching in fitness-tracker data	Jan 1, 2021	Activity RecognitionAnomaly Detection	—Unverified	0
Adaptive Activity Monitoring with Uncertainty Quantification in Switching Gaussian Process Models	Jan 8, 2019	Activity RecognitionGaussian Processes	—Unverified	0
Adaptation of Surgical Activity Recognition Models Across Operating Rooms	Jul 7, 2022	Activity RecognitionDomain Adaptation	—Unverified	0
An Interval-Based Bayesian Generative Model for Human Complex Activity Recognition	Jan 4, 2017	Activity RecognitionDiversity	—Unverified	0
ActiLabel: A Combinatorial Transfer Learning Framework for Activity Recognition	Mar 16, 2020	Activity RecognitionDiversity	—Unverified	0
A Semi-supervised Approach for Activity Recognition from Indoor Trajectory Data	Jan 9, 2023	Activity RecognitionDecision Making	—Unverified	0

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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