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 151–200 of 1322 papers

TitleStatusHype
A compressive multi-kernel method for privacy-preserving machine learning—0
Bubblenet: A Disperse Recurrent Structure To Recognize Activities—0
Activity Recognition on a Large Scale in Short Videos - Moments in Time Dataset—0
Analysis of Gait Pattern to Recognize the Human Activities—0
A Comprehensive Review of Automated Data Annotation Techniques in Human Activity Recognition—0
An adaptable cognitive microcontroller node for fitness activity recognition—0
An Actor-Centric Causality Graph for Asynchronous Temporal Inference in Group Activity—0
Activity Recognition in Assembly Tasks by Bayesian Filtering in Multi-Hypergraphs—0
A Comprehensive Overview on UWB Radar: Applications, Standards, Signal Processing Techniques, Datasets, Radio Chips, Trends and Future Research Directions—0
An Activity Recognition Framework for Continuous Monitoring of Non-Steady-State Locomotion of Individuals with Parkinson's Disease—0
A Multi-Task Deep Learning Approach for Sensor-based Human Activity Recognition and Segmentation—0
Activity recognition from videos with parallel hypergraph matching on GPUs—0
A Multi-Stream Convolutional Neural Network Framework for Group Activity Recognition—0
A Multi-Modal Explainability Approach for Human-Aware Robots in Multi-Party Conversation—0
Activity Recognition From Newborn Resuscitation Videos—0
Boosted Multiple Kernel Learning for First-Person Activity Recognition—0
A MIMO Radar-Based Metric Learning Approach for Activity Recognition—0
Am I fit for this physical activity? Neural embedding of physical conditioning from inertial sensors—0
"Filling the Blanks'': Identifying Micro-activities that Compose Complex Human Activities of Daily Living—0
Activity Recognition based on a Magnitude-Orientation Stream Network—0
A Comprehensive Methodological Survey of Human Activity Recognition Across Divers Data Modalities—0
Bonn Activity Maps: Dataset Description—0
Maximum Likelihood Speed Estimation of Moving Objects in Video Signals—0
Activity Recognition and Prediction in Real Homes—0
3D Human motion anticipation and classification—0
ActivityNet Challenge 2017 Summary—0
Augmenting Vision-Based Human Pose Estimation with Rotation Matrix—0
Model enhancement and personalization using weakly supervised learning for multi-modal mobile sensing—0
BON: An extended public domain dataset for human activity recognition—0
Boosted Markov Networks for Activity Recognition—0
Boosting Adversarial Transferability for Skeleton-based Action Recognition via Exploring the Model Posterior Space—0
CamLoc: Pedestrian Location Detection from Pose Estimation on Resource-constrained Smart-cameras—0
A Logic Programming Approach to Activity Recognition—0
Activity Monitoring of Islamic Prayer (Salat) Postures using Deep Learning—0
A comparative study on wearables and single-camera video for upper-limb out-of-thelab activity recognition with different deep learning architectures—0
Bilinear Programming for Human Activity Recognition with Unknown MRF Graphs—0
Attributes for Improved Attributes: A Multi-Task Network for Attribute Classification—0
Attentive pooling for Group Activity Recognition—0
Augmenting Bag-of-Words: Data-Driven Discovery of Temporal and Structural Information for Activity Recognition—0
Augmenting Deep Learning Adaptation for Wearable Sensor Data through Combined Temporal-Frequency Image Encoding—0
A Lightweight Deep Learning Model for Human Activity Recognition on Edge Devices—0
AI-Powered Non-Contact In-Home Gait Monitoring and Activity Recognition System Based on mm-Wave FMCW Radar and Cloud Computing—0
A Masked Semi-Supervised Learning Approach for Otago Micro Labels Recognition—0
Automated Activity Recognition in Clinical Documents—0
Automated Activity Recognition of Construction Equipment Using a Data Fusion Approach—0
Automated Human Activity Recognition by Colliding Bodies Optimization-based Optimal Feature Selection with Recurrent Neural Network—0
Automated Level Crossing System: A Computer Vision Based Approach with Raspberry Pi Microcontroller—0
Automated Surgical Activity Recognition with One Labeled Sequence—0
WearableMil: An End-to-End Framework for Military Activity Recognition and Performance Monitoring—0
Attention-Driven Body Pose Encoding for Human Activity Recognition—0
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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