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 451–500 of 1322 papers

TitleStatusHype
AVD: Adversarial Video Distillation—0
Enabling Edge Cloud Intelligence for Activity Learning in Smart Home—0
Enabling Machine Learning Across Heterogeneous Sensor Networks with Graph Autoencoders—0
Encoding Based Saliency Detection for Videos and Images—0
Energy Expenditure Estimation Through Daily Activity Recognition Using a Smart-phone—0
Enforcing Fundamental Relations via Adversarial Attacks on Input Parameter Correlations—0
Am I fit for this physical activity? Neural embedding of physical conditioning from inertial sensors—0
Domain Adaptation for Inertial Measurement Unit-based Human Activity Recognition: A Survey—0
Enhancing Smart Environments with Context-Aware Chatbots using Large Language Models—0
Automatic Operating Room Surgical Activity Recognition for Robot-Assisted Surgery—0
EnHDC: Ensemble Learning for Brain-Inspired Hyperdimensional Computing—0
"Filling the Blanks'': Identifying Micro-activities that Compose Complex Human Activities of Daily Living—0
DNN Transfer Learning from Diversified Micro-Doppler for Motion Classification—0
Entropy Decision Fusion for Smartphone Sensor based Human Activity Recognition—0
DIVERSIFY to Generalize: Learning Generalized Representations for Time Series Classification—0
ESPARGOS: An Ultra Low-Cost, Realtime-Capable Multi-Antenna WiFi Channel Sounder—0
Automatic Interaction and Activity Recognition from Videos of Human Manual Demonstrations with Application to Anomaly Detection—0
Estimating Human Poses Across Datasets: A Unified Skeleton and Multi-Teacher Distillation Approach—0
DIVERSIFY: A General Framework for Time Series Out-of-distribution Detection and Generalization—0
Evaluating Deep Neural Network Ensembles by Majority Voting cum Meta-Learning scheme—0
Diverse Intra- and Inter-Domain Activity Style Fusion for Cross-Person Generalization in Activity Recognition—0
WearableMil: An End-to-End Framework for Military Activity Recognition and Performance Monitoring—0
Evaluation of Encoding Schemes on Ubiquitous Sensor Signal for Spiking Neural Network—0
Evaluation of Regularization-based Continual Learning Approaches: Application to HAR—0
Event and Activity Recognition in Video Surveillance for Cyber-Physical Systems—0
Event-LSTM: An Unsupervised and Asynchronous Learning-based Representation for Event-based Data—0
EventSleep: Sleep Activity Recognition with Event Cameras—0
Evolving Markov Chains: Unsupervised Mode Discovery and Recognition from Data Streams—0
Activity Recognition based on a Magnitude-Orientation Stream Network—0
A Comprehensive Methodological Survey of Human Activity Recognition Across Divers Data Modalities—0
Distribution estimation and change-point estimation for time series via DNN-based GANs—0
Explainable Artificial Intelligence for Quantifying Interfering and High-Risk Behaviors in Autism Spectrum Disorder in a Real-World Classroom Environment Using Privacy-Preserving Video Analysis—0
Explainable Deep Learning Framework for Human Activity Recognition—0
Explaining, Analyzing, and Probing Representations of Self-Supervised Learning Models for Sensor-based Human Activity Recognition—0
Automated Surgical Activity Recognition with One Labeled Sequence—0
Explaining Motion Relevance for Activity Recognition in Video Deep Learning Models—0
Distributionally Robust Semi-Supervised Learning for People-Centric Sensing—0
Exploring Automatic Gym Workouts Recognition Locally On Wearable Resource-Constrained Devices—0
Automated Level Crossing System: A Computer Vision Based Approach with Raspberry Pi Microcontroller—0
Exploring FMCW Radars and Feature Maps for Activity Recognition: A Benchmark Study—0
Exploring the Capabilities of LLMs for IMU-based Fine-grained Human Activity Understanding—0
Exploring the Impact of Synthetic Data on Human Gesture Recognition Tasks Using GANs—0
Exploring Transformers for On-Line Handwritten Signature Verification—0
Maximum Likelihood Speed Estimation of Moving Objects in Video Signals—0
Extreme Low Resolution Activity Recognition with Multi-Siamese Embedding Learning—0
Extreme Low Resolution Activity Recognition with Confident Spatial-Temporal Attention Transfer—0
Distributed Agent-Based Collaborative Learning in Cross-Individual Wearable Sensor-Based Human Activity Recognition—0
Fast Low-parameter Video Activity Localization in Collaborative Learning Environments—0
Distilled Mid-Fusion Transformer Networks for Multi-Modal Human Activity Recognition—0
Automated Human Activity Recognition by Colliding Bodies Optimization-based Optimal Feature Selection with Recurrent Neural Network—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