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 371–380 of 1322 papers

Title	Date	Tasks	Status
Analyzing and Exploiting NARX Recurrent Neural Networks for Long-Term Dependencies	Feb 24, 2017	Activity RecognitionMachine Translation	—Unverified
Activity Recognition Using A Combination of Category Components And Local Models for Video Surveillance	Feb 28, 2015	Activity Recognition	—Unverified
A compressive multi-kernel method for privacy-preserving machine learning	Jun 20, 2021	Activity RecognitionBIG-bench Machine Learning	—Unverified
A Causality-Aware Pattern Mining Scheme for Group Activity Recognition in a Pervasive Sensor Space	Dec 1, 2023	Activity RecognitionGroup Activity Recognition	—Unverified
Bi-Causal: Group Activity Recognition via Bidirectional Causality	Jan 1, 2024	Activity RecognitionGroup 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
Beyond Isolated Frames: Enhancing Sensor-Based Human Activity Recognition through Intra- and Inter-Frame Attention	May 21, 2024	Activity RecognitionHuman Activity Recognition	—Unverified
Analysis of Gait Pattern to Recognize the Human Activities	Jul 18, 2014	Activity RecognitionHuman Activity Recognition	—Unverified
Activity Recognition on a Large Scale in Short Videos - Moments in Time Dataset	Sep 1, 2018	Action RecognitionActivity Recognition	—Unverified
Beyond Confusion: A Fine-grained Dialectical Examination of Human Activity Recognition Benchmark Datasets	Dec 12, 2024	Activity RecognitionHuman 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