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 51–75 of 1322 papers

TitleStatusHype
ATARS: An Aerial Traffic Atomic Activity Recognition and Temporal Segmentation DatasetCode0
PIM: Physics-Informed Multi-task Pre-training for Improving Inertial Sensor-Based Human Activity Recognition—0
Process Optimization and Deployment for Sensor-Based Human Activity Recognition Based on Deep Learning—0
DGAR: A Unified Domain Generalization Framework for RF-Enabled Human Activity Recognition—0
Multi-Modal Gesture Recognition from Video and Surgical Tool Pose Information via Motion Invariants—0
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
COMODO: Cross-Modal Video-to-IMU Distillation for Efficient Egocentric Human Activity RecognitionCode1
SGA-INTERACT: A 3D Skeleton-based Benchmark for Group Activity Understanding in Modern Basketball TacticCode1
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
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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