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 881–890 of 1322 papers

Title	Date	Tasks	Status
SWTF: Sparse Weighted Temporal Fusion for Drone-Based Activity Recognition	Nov 10, 2022	Activity RecognitionHuman Activity Recognition	—Unverified
Tapestry of Time and Actions: Modeling Human Activity Sequences using Temporal Point Process Flows	Jul 13, 2023	Activity RecognitionPrediction	—Unverified
TASKED: Transformer-based Adversarial learning for human activity recognition using wearable sensors via Self-KnowledgE Distillation	Sep 14, 2022	Activity RecognitionHuman Activity Recognition	—Unverified
WiFi-TCN: Temporal Convolution for Human Interaction Recognition based on WiFi signal	May 21, 2023	Activity RecognitionHuman Activity Recognition	—Unverified
Template co-updating in multi-modal human activity recognition systems	Dec 4, 2019	Activity RecognitionHuman Activity Recognition	—Unverified
Temporal Action Segmentation with High-level Complex Activity Labels	Aug 15, 2021	Action RecognitionAction Segmentation	—Unverified
Temporal DINO: A Self-supervised Video Strategy to Enhance Action Prediction	Aug 8, 2023	Activity RecognitionAutonomous Driving	—Unverified
Temporally Consistent Dynamic Scene Graphs: An End-to-End Approach for Action Tracklet Generation	Dec 3, 2024	Activity RecognitionAutonomous Navigation	—Unverified
Temporally Robust Global Motion Compensation by Keypoint-based Congealing	Mar 12, 2016	Activity RecognitionHuman Activity Recognition	—Unverified
Temporal Reasoning Graph for Activity Recognition	Aug 27, 2019	Action RecognitionActivity 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