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 821–830 of 1322 papers

Title	Date	Tasks	Status	Hype
Post-training Iterative Hierarchical Data Augmentation for Deep Networks	Dec 1, 2020	Activity RecognitionData Augmentation	—Unverified	0
Exploring Contrastive Learning in Human Activity Recognition for Healthcare	Nov 23, 2020	Activity RecognitionContrastive Learning	CodeCode Available	1
LaHAR: Latent Human Activity Recognition using LDA	Nov 23, 2020	Activity RecognitionClustering	CodeCode Available	0
Yet it moves: Learning from Generic Motions to Generate IMU data from YouTube videos	Nov 23, 2020	Activity RecognitionHuman Activity Recognition	—Unverified	0
Self-Supervised Transformers for Activity Classification using Ambient Sensors	Nov 22, 2020	Activity RecognitionAnomaly Detection	—Unverified	0
Self-supervised transfer learning of physiological representations from free-living wearable data	Nov 18, 2020	Activity RecognitionHuman Activity Recognition	CodeCode Available	1
Human activity recognition using improved dynamic image	Nov 15, 2020	Action RecognitionActivity Recognition	—Unverified	0
Generic Semi-Supervised Adversarial Subject Translation for Sensor-Based Human Activity Recognition	Nov 11, 2020	Activity RecognitionDomain Adaptation	—Unverified	0
Skeleton-based Relational Reasoning for Group Activity Analysis	Nov 11, 2020	Activity RecognitionGroup Activity Recognition	—Unverified	0
Learning Generalizable Physiological Representations from Large-scale Wearable Data	Nov 9, 2020	Activity RecognitionHuman Activity Recognition	CodeCode Available	1

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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