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 891–900 of 1322 papers

Title	Date	Tasks	Status	Hype
IMUTube: Automatic Extraction of Virtual on-body Accelerometry from Video for Human Activity Recognition	May 29, 2020	Activity RecognitionHuman Activity Recognition	—Unverified	0
Incremental Real-Time Personalization in Human Activity Recognition Using Domain Adaptive Batch Normalization	May 25, 2020	Activity RecognitionDomain Adaptation	—Unverified	0
Toward Automated Classroom Observation: Multimodal Machine Learning to Estimate CLASS Positive Climate and Negative Climate	May 19, 2020	Activity RecognitionBIG-bench Machine Learning	—Unverified	0
Sensor Data for Human Activity Recognition: Feature Representation and Benchmarking	May 15, 2020	Activity RecognitionBenchmarking	—Unverified	0
Enabling Edge Cloud Intelligence for Activity Learning in Smart Home	May 14, 2020	Activity Recognition	—Unverified	0
Utility-aware Privacy-preserving Data Releasing	May 9, 2020	Activity RecognitionHuman Activity Recognition	—Unverified	0
Layer-wise training convolutional neural networks with smaller filters for human activity recognition using wearable sensors	May 8, 2020	Activity RecognitionHuman Activity Recognition	—Unverified	0
Approaches and Applications of Early Classification of Time Series: A Review	May 6, 2020	Activity RecognitionClassification	—Unverified	0
Don't Explain without Verifying Veracity: An Evaluation of Explainable AI with Video Activity Recognition	May 5, 2020	Activity RecognitionDecision Making	—Unverified	0
SleepPoseNet: Multi-View Learning for Sleep Postural Transition Recognition Using UWB	May 2, 2020	Activity RecognitionData Augmentation	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