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 661–670 of 1322 papers

Title	Date	Tasks	Status
Benchmark of DNN Model Search at Deployment Time	Jun 1, 2022	Activity Recognitionmodel	—Unverified
Ultra-compact Binary Neural Networks for Human Activity Recognition on RISC-V Processors	May 25, 2022	Activity RecognitionHuman Activity Recognition	CodeCode Available
A Wireless-Vision Dataset for Privacy Preserving Human Activity Recognition	May 24, 2022	Action SegmentationActivity Recognition	—Unverified
UMSNet: An Universal Multi-sensor Network for Human Activity Recognition	May 24, 2022	Activity RecognitionHuman Activity Recognition	—Unverified
Classifying Human Activities using Machine Learning and Deep Learning Techniques	May 19, 2022	Activity RecognitionBIG-bench Machine Learning	—Unverified
The ACM Multimedia 2022 Computational Paralinguistics Challenge: Vocalisations, Stuttering, Activity, & Mosquitoes	May 13, 2022	Activity RecognitionHuman Activity Recognition	—Unverified
SPARCS: A Sparse Recovery Approach for Integrated Communication and Human Sensing in mmWave Systems	May 6, 2022	Activity RecognitionHuman Activity Recognition	—Unverified
Koopman pose predictions for temporally consistent human walking estimations	May 5, 2022	Activity RecognitionPose Prediction	—Unverified
An Empirical Study on Activity Recognition in Long Surgical Videos	May 5, 2022	Action DetectionActivity Detection	—Unverified
Resource-Eficient Continual Learning for Sensor-Based Human Activity Recognition	Apr 30, 2022	Activity RecognitionContinual Learning	—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