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 571–580 of 1322 papers

Title	Date	Tasks	Status
A Semi-supervised Approach for Activity Recognition from Indoor Trajectory Data	Jan 9, 2023	Activity RecognitionDecision Making	—Unverified
Unleashing the Power of Shared Label Structures for Human Activity Recognition	Jan 1, 2023	Activity RecognitionDecoder	—Unverified
An Actor-Centric Causality Graph for Asynchronous Temporal Inference in Group Activity	Jan 1, 2023	Activity RecognitionGroup Activity Recognition	—Unverified
SkeleTR: Towards Skeleton-based Action Recognition in the Wild	Jan 1, 2023	Action ClassificationAction Detection	—Unverified
Cross-modal Scalable Hierarchical Clustering in Hyperbolic space	Jan 1, 2023	Activity RecognitionClustering	—Unverified
Ordered Atomic Activity for Fine-grained Interactive Traffic Scenario Understanding	Jan 1, 2023	Activity Recognition	—Unverified
Human Activity Recognition from Wi-Fi CSI Data Using Principal Component-Based Wavelet CNN	Dec 26, 2022	Activity RecognitionHuman Activity Recognition	—Unverified
Human Activity Recognition in an Open World	Dec 23, 2022	Activity RecognitionHuman Activity Recognition	CodeCode Available
Simple Yet Surprisingly Effective Training Strategies for LSTMs in Sensor-Based Human Activity Recognition	Dec 23, 2022	Activity RecognitionHuman Activity Recognition	—Unverified
Graph Neural Network based Child Activity Recognition	Dec 18, 2022	Activity RecognitionGraph Neural Network	—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