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 1031–1040 of 1322 papers

Title	Date	Tasks	Status
Activity Recognition From Newborn Resuscitation Videos	Mar 14, 2023	Activity Recognitionobject-detection	—Unverified
Activity recognition from videos with parallel hypergraph matching on GPUs	May 4, 2015	Activity RecognitionGPU	—Unverified
Activity Recognition in Assembly Tasks by Bayesian Filtering in Multi-Hypergraphs	Feb 1, 2022	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
Activity Recognition Using A Combination of Category Components And Local Models for Video Surveillance	Feb 28, 2015	Activity Recognition	—Unverified
Activity recognition using ST-GCN with 3D motion data	Sep 13, 2019	Activity RecognitionMultimodal Activity Recognition	—Unverified
Activity Recognition with Moving Cameras and Few Training Examples: Applications for Detection of Autism-Related Headbanging	Jan 10, 2021	Action DetectionActivity Detection	—Unverified
Actor-Transformers for Group Activity Recognition	Mar 28, 2020	Activity RecognitionGroup Activity Recognition	—Unverified
AdaFPP: Adapt-Focused Bi-Propagating Prototype Learning for Panoramic Activity Recognition	May 4, 2024	Activity RecognitionBlocking	—Unverified
Adaptation of Surgical Activity Recognition Models Across Operating Rooms	Jul 7, 2022	Activity RecognitionDomain Adaptation	—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