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 581–590 of 1322 papers

Title	Date	Tasks	Status
OpenPack: A Large-scale Dataset for Recognizing Packaging Works in IoT-enabled Logistic Environments	Dec 10, 2022	Activity RecognitionHuman Activity Recognition	—Unverified
Deep Learning for Inertial Sensor Alignment	Dec 10, 2022	Activity RecognitionDeep Learning	—Unverified
Towards Stroke Patients' Upper-limb Automatic Motor Assessment Using Smartwatches	Dec 9, 2022	Activity RecognitionHuman Activity Recognition	—Unverified
Automated Level Crossing System: A Computer Vision Based Approach with Raspberry Pi Microcontroller	Dec 8, 2022	Activity Recognitionobject-detection	—Unverified
DroneAttention: Sparse Weighted Temporal Attention for Drone-Camera Based Activity Recognition	Dec 7, 2022	Activity RecognitionHuman Activity Recognition	—Unverified
Day2Dark: Pseudo-Supervised Activity Recognition beyond Silent Daylight	Dec 5, 2022	Activity RecognitionDomain Adaptation	—Unverified
Applications of human activity recognition in industrial processes -- Synergy of human and technology	Dec 5, 2022	Activity RecognitionHuman Activity Recognition	—Unverified
Recognition and Prediction of Surgical Gestures and Trajectories Using Transformer Models in Robot-Assisted Surgery	Dec 3, 2022	Activity RecognitionGesture Recognition	—Unverified
Video-based Pose-Estimation Data as Source for Transfer Learning in Human Activity Recognition	Dec 2, 2022	Activity RecognitionHuman Activity Recognition	—Unverified
Gated Recurrent Neural Networks with Weighted Time-Delay Feedback	Dec 1, 2022	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