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 171–180 of 1322 papers

Title	Date	Tasks	Status
Activity Recognition based on a Magnitude-Orientation Stream Network	Aug 22, 2017	Activity RecognitionOptical Flow Estimation	—Unverified
A Comprehensive Methodological Survey of Human Activity Recognition Across Divers Data Modalities	Sep 15, 2024	Action DetectionActivity Detection	—Unverified
A Wireless-Vision Dataset for Privacy Preserving Human Activity Recognition	May 24, 2022	Action SegmentationActivity Recognition	—Unverified
Maximum Likelihood Speed Estimation of Moving Objects in Video Signals	Mar 10, 2020	Activity RecognitionHuman Activity Recognition	—Unverified
Activity Recognition and Prediction in Real Homes	May 20, 2019	Activity RecognitionPrediction	—Unverified
3D Human motion anticipation and classification	Dec 31, 2020	Action RecognitionActivity Recognition	—Unverified
A Masked Semi-Supervised Learning Approach for Otago Micro Labels Recognition	May 21, 2024	Activity RecognitionHuman Activity Recognition	—Unverified
ActivityNet Challenge 2017 Summary	Oct 22, 2017	Activity Recognition	—Unverified
Model enhancement and personalization using weakly supervised learning for multi-modal mobile sensing	Oct 29, 2019	Activity RecognitionWeakly-supervised Learning	—Unverified
Background Knowledge Injection for Interpretable Sequence Classification	Jun 25, 2020	Activity RecognitionClassification	—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