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 591–600 of 1322 papers

Title	Date	Tasks	Status	Hype
Beyond the Gates of Euclidean Space: Temporal-Discrimination-Fusions and Attention-based Graph Neural Network for Human Activity Recognition	Jun 10, 2022	Activity RecognitionGraph Neural Network	—Unverified	0
PrivHAR: Recognizing Human Actions From Privacy-preserving Lens	Jun 8, 2022	Action RecognitionActivity Recognition	—Unverified	0
Two-stage Human Activity Recognition on Microcontrollers with Decision Trees and CNNs	Jun 7, 2022	Activity RecognitionHuman Activity Recognition	—Unverified	0
Self-supervised Learning for Human Activity Recognition Using 700,000 Person-days of Wearable Data	Jun 6, 2022	Activity RecognitionHuman Activity Recognition	CodeCode Available	1
Human Activity Recognition on Time Series Accelerometer Sensor Data using LSTM Recurrent Neural Networks	Jun 3, 2022	Activity RecognitionHuman Activity Recognition	—Unverified	0
Benchmark of DNN Model Search at Deployment Time	Jun 1, 2022	Activity Recognitionmodel	—Unverified	0
Ultra-compact Binary Neural Networks for Human Activity Recognition on RISC-V Processors	May 25, 2022	Activity RecognitionHuman Activity Recognition	CodeCode Available	0
A Wireless-Vision Dataset for Privacy Preserving Human Activity Recognition	May 24, 2022	Action SegmentationActivity Recognition	—Unverified	0
UMSNet: An Universal Multi-sensor Network for Human Activity Recognition	May 24, 2022	Activity RecognitionHuman Activity Recognition	—Unverified	0
Contrastive Learning with Cross-Modal Knowledge Mining for Multimodal Human Activity Recognition	May 20, 2022	Activity RecognitionContrastive Learning	CodeCode Available	1

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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