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

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Showing 141–150 of 1322 papers

Title	Date	Tasks	Status
An Event Calculus Production Rule System for Reasoning in Dynamic and Uncertain Domains	Dec 14, 2015	Activity RecognitionEpistemic Reasoning	—Unverified
A Neurorobotics Approach to Behaviour Selection based on Human Activity Recognition	Jul 27, 2021	Activity RecognitionDecision Making	—Unverified
Activity Recognition with Moving Cameras and Few Training Examples: Applications for Detection of Autism-Related Headbanging	Jan 10, 2021	Action DetectionActivity Detection	—Unverified
A Tree-structure Convolutional Neural Network for Temporal Features Exaction on Sensor-based Multi-resident Activity Recognition	Nov 5, 2020	Activity RecognitionTime Series	—Unverified
An end-to-end (deep) neural network applied to raw EEG, fNIRs and body motion data for data fusion and BCI classification task without any pre-/post-processing	Jul 17, 2019	Action DetectionActivity Recognition	—Unverified
An Efficient Data Imputation Technique for Human Activity Recognition	Jul 8, 2020	Activity RecognitionBIG-bench Machine Learning	—Unverified
Activity recognition using ST-GCN with 3D motion data	Sep 13, 2019	Activity RecognitionMultimodal Activity Recognition	—Unverified
Activity Recognition Using A Combination of Category Components And Local Models for Video Surveillance	Feb 28, 2015	Activity Recognition	—Unverified
Analyzing and Exploiting NARX Recurrent Neural Networks for Long-Term Dependencies	Feb 24, 2017	Activity RecognitionMachine Translation	—Unverified
A compressive multi-kernel method for privacy-preserving machine learning	Jun 20, 2021	Activity RecognitionBIG-bench Machine 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