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 671–680 of 1322 papers

Title	Date	Tasks	Status
The Sweet-Home speech and multimodal corpus for home automation interaction	May 1, 2014	Activity RecognitionDistant Speech Recognition	—Unverified
Three Branches: Detecting Actions With Richer Features	Aug 13, 2019	Action LocalizationActivity Recognition	—Unverified
Three-Stream Fusion Network for First-Person Interaction Recognition	Feb 19, 2020	Activity RecognitionHuman Interaction Recognition	—Unverified
Three-stream network for enriched Action Recognition	Apr 27, 2021	Action RecognitionActivity Recognition	—Unverified
Time Series Segmentation through Automatic Feature Learning	Jan 16, 2018	Activity RecognitionEEG	—Unverified
Time Series Similarity Score Functions to Monitor and Interact with the Training and Denoising Process of a Time Series Diffusion Model applied to a Human Activity Recognition Dataset based on IMUs	May 20, 2025	Activity RecognitionDenoising	—Unverified
Timestamp-supervised Wearable-based Activity Segmentation and Recognition with Contrastive Learning and Order-Preserving Optimal Transport	Oct 13, 2023	Activity RecognitionContrastive Learning	—Unverified
Too Good To Be True: performance overestimation in (re)current practices for Human Activity Recognition	Oct 18, 2023	Activity RecognitionHuman Activity Recognition	—Unverified
Topological Persistence Guided Knowledge Distillation for Wearable Sensor Data	Jul 7, 2024	Activity RecognitionDeep Learning	—Unverified
Toward Automated Classroom Observation: Multimodal Machine Learning to Estimate CLASS Positive Climate and Negative Climate	May 19, 2020	Activity RecognitionBIG-bench Machine Learning	—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