SOTAVerified

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 351–375 of 1322 papers

TitleStatusHype
Timestamp-supervised Wearable-based Activity Segmentation and Recognition with Contrastive Learning and Order-Preserving Optimal Transport—0
Antenna Response Consistency Driven Self-supervised Learning for WIFI-based Human Activity Recognition—0
Augmenting Vision-Based Human Pose Estimation with Rotation Matrix—0
Hadamard Domain Training with Integers for Class Incremental Quantized Learning—0
Otago Exercises Monitoring for Older Adults by a Single IMU and Hierarchical Machine Learning Models—0
Decoding Human Activities: Analyzing Wearable Accelerometer and Gyroscope Data for Activity Recognition—0
Black-box Attacks on Image Activity Prediction and its Natural Language Explanations—0
Investigating Deep Neural Network Architecture and Feature Extraction Designs for Sensor-based Human Activity Recognition—0
Finding Order in Chaos: A Novel Data Augmentation Method for Time Series in Contrastive LearningCode1
SkeleTR: Towrads Skeleton-based Action Recognition in the Wild—0
Human Activity Segmentation Challenge @ ECML/PKDD’23Code1
Forensic Video Analytic Software—0
Overview of Human Activity Recognition Using Sensor Data—0
Action Segmentation Using 2D Skeleton Heatmaps and Multi-Modality Fusion—0
Grey-box Bayesian Optimization for Sensor Placement in Assisted Living Environments—0
Multimodal Contrastive Learning with Hard Negative Sampling for Human Activity Recognition—0
Expanding Frozen Vision-Language Models without Retraining: Towards Improved Robot Perception—0
Robust Activity Recognition for Adaptive Worker-Robot Interaction using Transfer Learning—0
Large Transformers are Better EEG LearnersCode0
Hard No-Box Adversarial Attack on Skeleton-Based Human Action Recognition with Skeleton-Motion-Informed GradientCode1
Temporal DINO: A Self-supervised Video Strategy to Enhance Action Prediction—0
Worker Activity Recognition in Manufacturing Line Using Near-body Electric Field—0
Weakly Supervised Multi-Task Representation Learning for Human Activity Analysis Using Wearables—0
DIVERSIFY: A General Framework for Time Series Out-of-distribution Detection and Generalization—0
Evaluating Spiking Neural Network On Neuromorphic Platform For Human Activity RecognitionCode0
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All datasetsRWF-2000Stanford40First-Person Hand Action BenchmarkSelf-stimulatory Behavior Dataset

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1Structured Keypoint PoolingAccuracy93.4—Unverified
2Semi-Supervised Hard Attention (SSHA); pretrained on Deepmind Kinetics datasetAccuracy90.4—Unverified
3Human Skeletons + Change DetectionAccuracy90.25—Unverified
4Separable Convolutional LSTMAccuracy89.75—Unverified
5SPIL ConvolutionAccuracy89.3—Unverified
6Flow Gated NetworkAccuracy87.25—Unverified
#ModelMetricClaimedVerifiedStatus
1FocusCLIPTop-3 Accuracy (%)10.47—Unverified
2CLIPTop-3 Accuracy (%)6.49—Unverified
#ModelMetricClaimedVerifiedStatus
1Boutaleb et al.1:1 Accuracy97.91—Unverified
#ModelMetricClaimedVerifiedStatus
1all-landmark-modelActivity Recognition0.76—Unverified