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 751–760 of 1322 papers

Title	Date	Tasks	Status	Hype
A Feature Selection Method for Multi-Dimension Time-Series Data	Apr 22, 2021	Activity Recognitionfeature selection	—Unverified	0
Continual Learning in Sensor-based Human Activity Recognition: an Empirical Benchmark Analysis	Apr 19, 2021	Activity RecognitionContinual Learning	—Unverified	0
Self-Supervised WiFi-Based Activity Recognition	Apr 19, 2021	Activity RecognitionContrastive Learning	—Unverified	0
Ego-Exo: Transferring Visual Representations from Third-person to First-person Videos	Apr 16, 2021	Activity RecognitionDiversity	CodeCode Available	1
Spatiotemporal Deformable Scene Graphs for Complex Activity Detection	Apr 16, 2021	Action DetectionActivity Detection	—Unverified	0
Personalized Semi-Supervised Federated Learning for Human Activity Recognition	Apr 15, 2021	Active LearningActivity Recognition	—Unverified	0
Description of Structural Biases and Associated Data in Sensor-Rich Environments	Apr 11, 2021	Activity Recognition	—Unverified	0
Affinity-Based Hierarchical Learning of Dependent Concepts for Human Activity Recognition	Apr 11, 2021	Activity RecognitionGeneral Classification	—Unverified	0
Multi-GAT: A Graphical Attention-based Hierarchical Multimodal Representation Learning Approach for Human Activity Recognition	Apr 1, 2021	Activity RecognitionHuman Activity Recognition	—Unverified	0
Selective Feature Compression for Efficient Activity Recognition Inference	Apr 1, 2021	Action RecognitionActivity Recognition	—Unverified	0

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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