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 821–830 of 1322 papers

Title	Date	Tasks	Status
A*HAR: A New Benchmark towards Semi-supervised learning for Class-imbalanced Human Activity Recognition	Jan 13, 2021	Activity RecognitionHuman Activity Recognition	CodeCode Available
Activity Recognition with Moving Cameras and Few Training Examples: Applications for Detection of Autism-Related Headbanging	Jan 10, 2021	Action DetectionActivity Detection	—Unverified
Octave Mix: Data augmentation using frequency decomposition for activity recognition	Jan 8, 2021	Activity RecognitionData Augmentation	—Unverified
Anomaly Recognition from surveillance videos using 3D Convolutional Neural Networks	Jan 4, 2021	Activity RecognitionAnomaly Detection	—Unverified
Transformers in Vision: A Survey	Jan 4, 2021	Action RecognitionActivity Recognition	—Unverified
A Novel Multi-Stage Training Approach for Human Activity Recognition from Multimodal Wearable Sensor Data Using Deep Neural Network	Jan 3, 2021	Activity RecognitionHuman Activity Recognition	—Unverified
Uncertainty-sensitive Activity Recognition: a Reliability Benchmark and the CARING Models	Jan 2, 2021	Action RecognitionActivity Recognition	—Unverified
Context Aware Group Activity Recognition	Jan 1, 2021	Activity RecognitionGroup Activity Recognition	—Unverified
SkipW: Resource adaptable RNN with strict upper computational limit	Jan 1, 2021	Activity RecognitionHuman Activity Recognition	—Unverified
Anonymizing Egocentric Videos	Jan 1, 2021	Activity Recognitionobject-detection	—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