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 176–200 of 1322 papers

TitleStatusHype
HAR-DoReMi: Optimizing Data Mixture for Self-Supervised Human Activity Recognition Across Heterogeneous IMU Datasets—0
In Shift and In Variance: Assessing the Robustness of HAR Deep Learning Models against Variability—0
SurgicalVLM-Agent: Towards an Interactive AI Co-Pilot for Pituitary Surgery—0
PromptGAR: Flexible Promptive Group Activity Recognition—0
From Motion Signals to Insights: A Unified Framework for Student Behavior Analysis and Feedback in Physical Education Classes—0
Hybrid CNN-Dilated Self-attention Model Using Inertial and Body-Area Electrostatic Sensing for Gym Workout Recognition, Counting, and User AuthentificationCode0
Exploring FMCW Radars and Feature Maps for Activity Recognition: A Benchmark Study—0
CADDI: An in-Class Activity Detection Dataset using IMU data from low-cost sensors—0
SHADE-AD: An LLM-Based Framework for Synthesizing Activity Data of Alzheimer's Patients—0
GNN-XAR: A Graph Neural Network for Explainable Activity Recognition in Smart Homes—0
FinP: Fairness-in-Privacy in Federated Learning by Addressing Disparities in Privacy Risk—0
Random Projections and Natural Sparsity in Time-Series Classification: A Theoretical Analysis—0
On Neural Inertial Classification Networks for Pedestrian Activity Recognition—0
Enhancing Smart Environments with Context-Aware Chatbots using Large Language Models—0
Multi-view Video-Pose Pretraining for Operating Room Surgical Activity RecognitionCode0
ConSense: Continually Sensing Human Activity with WiFi via Growing and PickingCode0
Frequency-Aware Masked Autoencoders for Human Activity Recognition using Accelerometers—0
In-sensor 24 classes HAR under 850 Bytes—0
FlowAR: une plateforme uniformisée pour la reconnaissance des activités humaines à partir de capteurs binaires—0
ESPARGOS: An Ultra Low-Cost, Realtime-Capable Multi-Antenna WiFi Channel Sounder—0
Low-power Spike-based Wearable Analytics on RRAM Crossbars—0
SelaFD:Seamless Adaptation of Vision Transformer Fine-tuning for Radar-based Human ActivityCode0
CNN Autoencoders for Hierarchical Feature Extraction and Fusion in Multi-sensor Human Activity Recognition—0
Scaling laws in wearable human activity recognition—0
Assessing the Impact of Sampling Irregularity in Time Series Data: Human Activity Recognition As A Case Study—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