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 1141–1150 of 1322 papers

Title	Date	Tasks	Status
Augmenting Bag-of-Words: Data-Driven Discovery of Temporal and Structural Information for Activity Recognition	Oct 7, 2015	Activity Recognition	—Unverified
Augmenting Deep Learning Adaptation for Wearable Sensor Data through Combined Temporal-Frequency Image Encoding	Jul 3, 2023	Activity RecognitionDomain Adaptation	—Unverified
Augmenting Vision-Based Human Pose Estimation with Rotation Matrix	Oct 9, 2023	Activity RecognitionData Augmentation	—Unverified
Automated Activity Recognition in Clinical Documents	Oct 1, 2013	Activity Recognition	—Unverified
Automated Activity Recognition of Construction Equipment Using a Data Fusion Approach	Jun 5, 2019	Activity Recognition	—Unverified
Automated Human Activity Recognition by Colliding Bodies Optimization-based Optimal Feature Selection with Recurrent Neural Network	Oct 7, 2020	Activity Recognitionfeature selection	—Unverified
Automated Level Crossing System: A Computer Vision Based Approach with Raspberry Pi Microcontroller	Dec 8, 2022	Activity Recognitionobject-detection	—Unverified
Automated Surgical Activity Recognition with One Labeled Sequence	Jul 20, 2019	Activity Recognition	—Unverified
WearableMil: An End-to-End Framework for Military Activity Recognition and Performance Monitoring	Oct 7, 2024	Activity RecognitionCloud Computing	—Unverified
Automatic Interaction and Activity Recognition from Videos of Human Manual Demonstrations with Application to Anomaly Detection	Apr 19, 2023	Activity RecognitionAnomaly 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