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 951–960 of 1322 papers

Title	Date	Tasks	Status
Personalized Activity Recognition with Deep Triplet Embeddings	Jan 15, 2020	Action DetectionActivity Detection	CodeCode Available
Towards Generalizable Surgical Activity Recognition Using Spatial Temporal Graph Convolutional Networks	Jan 11, 2020	Activity RecognitionGesture Recognition	—Unverified
Classification of human activity recognition using smartphones	Jan 6, 2020	Activity RecognitionClassification	—Unverified
Improve Unsupervised Domain Adaptation with Mixup Training	Jan 3, 2020	Activity RecognitionDomain Adaptation	—Unverified
Feature engineering workflow for activity recognition from synchronized inertial measurement units	Dec 18, 2019	Activity RecognitionBIG-bench Machine Learning	CodeCode Available
Bonn Activity Maps: Dataset Description	Dec 13, 2019	Activity Recognition	—Unverified
Enabling Machine Learning Across Heterogeneous Sensor Networks with Graph Autoencoders	Dec 12, 2019	Activity RecognitionAnomaly Detection	—Unverified
DASZL: Dynamic Action Signatures for Zero-shot Learning	Dec 8, 2019	Action DetectionActivity Detection	—Unverified
Kernel learning for visual perception	Dec 6, 2019	Activity RecognitionHuman Activity Recognition	CodeCode Available
Hybrid Model Featuring CNN and LSTM Architecture for Human Activity Recognition on Smartphone Sensor Data	Dec 5, 2019	Activity RecognitionHuman Activity Recognition	—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