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 1221–1230 of 1322 papers

Title	Date	Tasks	Status
Detecting Unseen Falls from Wearable Devices using Channel-wise Ensemble of Autoencoders	Oct 12, 2016	Activity RecognitionOne-Class Classification	—Unverified
Dataiku's Solution to SPHERE's Activity Recognition Challenge	Oct 10, 2016	Activity RecognitionBIG-bench Machine Learning	—Unverified
Contextual Relationship-based Activity Segmentation on an Event Stream in the IoT Environment with Multi-user Activities	Sep 20, 2016	Activity RecognitionHuman Activity Recognition	—Unverified
A framework for mining process models from emails logs	Sep 20, 2016	Activity Recognition	—Unverified
Mouse Movement and Probabilistic Graphical Models Based E-Learning Activity Recognition Improvement Possibilistic Model	Aug 8, 2016	Activity Recognition	—Unverified
Learning Tree-Structured Detection Cascades for Heterogeneous Networks of Embedded Devices	Jul 30, 2016	Activity Recognition	—Unverified
Online Learning of Event Definitions	Jul 30, 2016	Activity RecognitionInductive logic programming	CodeCode Available
Impact of Physical Activity on Sleep:A Deep Learning Based Exploration	Jul 24, 2016	Activity RecognitionDeep Learning	—Unverified
Robust Automated Human Activity Recognition and its Application to Sleep Research	Jul 17, 2016	Activity RecognitionHuman Activity Recognition	—Unverified
Hierarchical Deep Temporal Models for Group Activity Recognition	Jul 9, 2016	Activity RecognitionGroup Activity Recognition	CodeCode Available

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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