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 601–610 of 1322 papers

Title	Date	Tasks	Status	Hype
Classifying Human Activities using Machine Learning and Deep Learning Techniques	May 19, 2022	Activity RecognitionBIG-bench Machine Learning	—Unverified	0
The ACM Multimedia 2022 Computational Paralinguistics Challenge: Vocalisations, Stuttering, Activity, & Mosquitoes	May 13, 2022	Activity RecognitionHuman Activity Recognition	—Unverified	0
SPARCS: A Sparse Recovery Approach for Integrated Communication and Human Sensing in mmWave Systems	May 6, 2022	Activity RecognitionHuman Activity Recognition	—Unverified	0
Koopman pose predictions for temporally consistent human walking estimations	May 5, 2022	Activity RecognitionPose Prediction	—Unverified	0
An Empirical Study on Activity Recognition in Long Surgical Videos	May 5, 2022	Action DetectionActivity Detection	—Unverified	0
Resource-Eficient Continual Learning for Sensor-Based Human Activity Recognition	Apr 30, 2022	Activity RecognitionContinual Learning	—Unverified	0
A Close Look into Human Activity Recognition Models using Deep Learning	Apr 26, 2022	Activity RecognitionDeep Learning	—Unverified	0
PhysioGAN: Training High Fidelity Generative Model for Physiological Sensor Readings	Apr 25, 2022	Activity RecognitionClassification	—Unverified	0
Ensemble diverse hypotheses and knowledge distillation for unsupervised cross-subject adaptation	Apr 15, 2022	Activity RecognitionDomain Adaptation	CodeCode Available	0
AutoFi: Towards Automatic WiFi Human Sensing via Geometric Self-Supervised Learning	Apr 12, 2022	Activity RecognitionDomain Adaptation	CodeCode Available	2

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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