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 711–720 of 1322 papers

Title	Date	Tasks	Status
Uncertainty-Aware Audiovisual Activity Recognition Using Deep Bayesian Variational Inference	Oct 1, 2019	Activity RecognitionBayesian Inference	—Unverified
Uncertainty aware audiovisual activity recognition using deep Bayesian variational inference	Nov 27, 2018	Activity RecognitionBayesian Inference	—Unverified
Uncertainty Quantification for Deep Context-Aware Mobile Activity Recognition and Unknown Context Discovery	Mar 3, 2020	Activity RecognitionClustering	—Unverified
Uncertainty-sensitive Activity Recognition: a Reliability Benchmark and the CARING Models	Jan 2, 2021	Action RecognitionActivity Recognition	—Unverified
Understanding and Improving Recurrent Networks for Human Activity Recognition by Continuous Attention	Oct 7, 2018	Activity RecognitionHuman Activity Recognition	—Unverified
Understanding Human Activity with Uncertainty Measure for Novelty in Graph Convolutional Networks	Oct 10, 2024	Activity RecognitionHuman Activity Recognition	—Unverified
Unified Keypoint-based Action Recognition Framework via Structured Keypoint Pooling	Mar 27, 2023	Action LocalizationAction Recognition	—Unverified
UniMiB SHAR: a new dataset for human activity recognition using acceleration data from smartphones	Nov 23, 2016	Activity RecognitionGeneral Classification	—Unverified
Unimodal and Multimodal Sensor Fusion for Wearable Activity Recognition	Apr 24, 2024	Activity RecognitionGesture Recognition	—Unverified
Unsupervised Deep Anomaly Detection for Multi-Sensor Time-Series Signals	Jul 27, 2021	Activity RecognitionAnomaly Detection	—Unverified

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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