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 181–190 of 1322 papers

Title	Date	Tasks	Status
Beyond Confusion: A Fine-grained Dialectical Examination of Human Activity Recognition Benchmark Datasets	Dec 12, 2024	Activity RecognitionHuman Activity Recognition	—Unverified
Beyond the Gates of Euclidean Space: Temporal-Discrimination-Fusions and Attention-based Graph Neural Network for Human Activity Recognition	Jun 10, 2022	Activity RecognitionGraph Neural Network	—Unverified
BON: An extended public domain dataset for human activity recognition	Sep 12, 2022	Activity RecognitionHuman Activity Recognition	—Unverified
Augmenting Bag-of-Words: Data-Driven Discovery of Temporal and Structural Information for Activity Recognition	Oct 7, 2015	Activity Recognition	—Unverified
A Logic Programming Approach to Activity Recognition	May 28, 2009	Activity Recognition	—Unverified
Activity Monitoring of Islamic Prayer (Salat) Postures using Deep Learning	Nov 11, 2019	Activity RecognitionDeep Learning	—Unverified
A comparative study on wearables and single-camera video for upper-limb out-of-thelab activity recognition with different deep learning architectures	Feb 4, 2024	Activity Recognition	—Unverified
Attributes for Improved Attributes: A Multi-Task Network for Attribute Classification	Apr 25, 2016	Activity RecognitionAttribute	—Unverified
Attentive pooling for Group Activity Recognition	Aug 31, 2022	Activity RecognitionGroup Activity Recognition	—Unverified
A Lightweight Deep Learning Model for Human Activity Recognition on Edge Devices	Sep 20, 2019	Activity RecognitionDeep Learning	—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