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 781–790 of 1322 papers

Title	Date	Tasks	Status
Evaluating Deep Neural Network Ensembles by Majority Voting cum Meta-Learning scheme	May 9, 2021	Activity RecognitionHuman Activity Recognition	—Unverified
Human Activity Recognition Models in Ontology Networks	May 5, 2021	Activity RecognitionHuman Activity Recognition	—Unverified
Activity-Aware Deep Cognitive Fatigue Assessment using Wearables	May 5, 2021	Activity Recognition	—Unverified
Three-stream network for enriched Action Recognition	Apr 27, 2021	Action RecognitionActivity Recognition	—Unverified
A Feature Selection Method for Multi-Dimension Time-Series Data	Apr 22, 2021	Activity Recognitionfeature selection	—Unverified
Continual Learning in Sensor-based Human Activity Recognition: an Empirical Benchmark Analysis	Apr 19, 2021	Activity RecognitionContinual Learning	—Unverified
Self-Supervised WiFi-Based Activity Recognition	Apr 19, 2021	Activity RecognitionContrastive Learning	—Unverified
Spatiotemporal Deformable Scene Graphs for Complex Activity Detection	Apr 16, 2021	Action DetectionActivity Detection	—Unverified
Personalized Semi-Supervised Federated Learning for Human Activity Recognition	Apr 15, 2021	Active LearningActivity Recognition	—Unverified
Description of Structural Biases and Associated Data in Sensor-Rich Environments	Apr 11, 2021	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