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 521–530 of 1322 papers

Title	Date	Tasks	Status
Augmenting Vision-Based Human Pose Estimation with Rotation Matrix	Oct 9, 2023	Activity RecognitionData Augmentation	—Unverified
A Masked Semi-Supervised Learning Approach for Otago Micro Labels Recognition	May 21, 2024	Activity RecognitionHuman Activity Recognition	—Unverified
ActivityNet Challenge 2017 Summary	Oct 22, 2017	Activity Recognition	—Unverified
3D Human motion anticipation and classification	Dec 31, 2020	Action RecognitionActivity Recognition	—Unverified
Model enhancement and personalization using weakly supervised learning for multi-modal mobile sensing	Oct 29, 2019	Activity RecognitionWeakly-supervised Learning	—Unverified
Differential Recurrent Neural Network and its Application for Human Activity Recognition	May 9, 2019	Activity RecognitionHuman Activity Recognition	—Unverified
Digging Deeper into Egocentric Gaze Prediction	Apr 12, 2019	Activity RecognitionGaze Prediction	—Unverified
Augmenting Deep Learning Adaptation for Wearable Sensor Data through Combined Temporal-Frequency Image Encoding	Jul 3, 2023	Activity RecognitionDomain Adaptation	—Unverified
Differentially Private Video Activity Recognition	Jun 27, 2023	Activity RecognitionClassification	—Unverified
Augmenting Bag-of-Words: Data-Driven Discovery of Temporal and Structural Information for Activity Recognition	Oct 7, 2015	Activity Recognition	—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