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 201–210 of 1322 papers

Title	Date	Tasks	Status
Automatic Operating Room Surgical Activity Recognition for Robot-Assisted Surgery	Jun 29, 2020	Action RecognitionActivity Recognition	—Unverified
A Lightweight Deep Learning Model for Human Activity Recognition on Edge Devices	Sep 20, 2019	Activity RecognitionDeep Learning	—Unverified
AI-Powered Non-Contact In-Home Gait Monitoring and Activity Recognition System Based on mm-Wave FMCW Radar and Cloud Computing	Aug 11, 2022	Activity RecognitionCloud Computing	—Unverified
A Wearable Multi-Modal Edge-Computing System for Real-Time Kitchen Activity Recognition	Sep 10, 2024	Activity RecognitionEdge-computing	—Unverified
A Wi-Fi Signal-Based Human Activity Recognition Using High-Dimensional Factor Models	Nov 10, 2023	Activity RecognitionHuman Activity Recognition	—Unverified
A Wireless-Vision Dataset for Privacy Preserving Human Activity Recognition	May 24, 2022	Action SegmentationActivity Recognition	—Unverified
Attention-Driven Body Pose Encoding for Human Activity Recognition	Sep 29, 2020	Activity RecognitionHuman Activity Recognition	—Unverified
Balancing Continual Learning and Fine-tuning for Human Activity Recognition	Jan 4, 2024	Activity RecognitionContinual Learning	—Unverified
Balancing Privacy and Action Performance: A Penalty-Driven Approach to Image Anonymization	Apr 19, 2025	Action RecognitionActivity Recognition	—Unverified
Activity Modeling in Smart Home using High Utility Pattern Mining over Data Streams	Jun 25, 2013	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