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 1071–1080 of 1322 papers

Title	Date	Tasks	Status
A Multi-Modal Explainability Approach for Human-Aware Robots in Multi-Party Conversation	May 20, 2024	Activity RecognitionBinary Classification	—Unverified
A Multi-Stream Convolutional Neural Network Framework for Group Activity Recognition	Dec 26, 2018	Activity RecognitionGroup Activity Recognition	—Unverified
A Multi-Task Deep Learning Approach for Sensor-based Human Activity Recognition and Segmentation	Mar 20, 2023	Activity RecognitionBenchmarking	—Unverified
An Activity Recognition Framework for Continuous Monitoring of Non-Steady-State Locomotion of Individuals with Parkinson's Disease	Oct 8, 2021	Activity Recognition	—Unverified
An Actor-Centric Causality Graph for Asynchronous Temporal Inference in Group Activity	Jan 1, 2023	Activity RecognitionGroup Activity Recognition	—Unverified
An adaptable cognitive microcontroller node for fitness activity recognition	Jan 13, 2022	Activity Recognition	—Unverified
Analysis of Gait Pattern to Recognize the Human Activities	Jul 18, 2014	Activity RecognitionHuman Activity Recognition	—Unverified
Analyzing and Exploiting NARX Recurrent Neural Networks for Long-Term Dependencies	Feb 24, 2017	Activity RecognitionMachine Translation	—Unverified
An Efficient Data Imputation Technique for Human Activity Recognition	Jul 8, 2020	Activity RecognitionBIG-bench Machine Learning	—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

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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