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 381–390 of 1322 papers

Title	Date	Tasks	Status
Unsupervised Embedding Learning for Human Activity Recognition Using Wearable Sensor Data	Jul 21, 2023	Activity RecognitionClustering	—Unverified
Self-Supervised Learning for WiFi CSI-Based Human Activity Recognition: A Systematic Study	Jul 19, 2023	Activity RecognitionHuman Activity Recognition	—Unverified
Siamese Networks for Weakly Supervised Human Activity Recognition	Jul 18, 2023	Activity RecognitionHuman Activity Recognition	—Unverified
randomHAR: Improving Ensemble Deep Learners for Human Activity Recognition with Sensor Selection and Reinforcement Learning	Jul 15, 2023	Activity RecognitionFeature Engineering	—Unverified
Tapestry of Time and Actions: Modeling Human Activity Sequences using Temporal Point Process Flows	Jul 13, 2023	Activity RecognitionPrediction	—Unverified
A Comprehensive Review of Automated Data Annotation Techniques in Human Activity Recognition	Jul 12, 2023	Activity RecognitionHuman Activity Recognition	—Unverified
Self-supervised Optimization of Hand Pose Estimation using Anatomical Features and Iterative Learning	Jul 6, 2023	Activity RecognitionHand Pose Estimation	—Unverified
A Real-time Human Pose Estimation Approach for Optimal Sensor Placement in Sensor-based Human Activity Recognition	Jul 6, 2023	Activity RecognitionHuman Activity Recognition	—Unverified
Exploring Transformers for On-Line Handwritten Signature Verification	Jul 4, 2023	Activity Recognition	—Unverified
Augmenting Deep Learning Adaptation for Wearable Sensor Data through Combined Temporal-Frequency Image Encoding	Jul 3, 2023	Activity RecognitionDomain Adaptation	—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