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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 1–25 of 1322 papers

TitleStatusHype
ZKP-FedEval: Verifiable and Privacy-Preserving Federated Evaluation using Zero-Knowledge Proofs—0
SEZ-HARN: Self-Explainable Zero-shot Human Activity Recognition NetworkCode0
Efficient Retail Video Annotation: A Robust Key Frame Generation Approach for Product and Customer Interaction Analysis—0
DeSPITE: Exploring Contrastive Deep Skeleton-Pointcloud-IMU-Text Embeddings for Advanced Point Cloud Human Activity Understanding—0
MORIC: CSI Delay-Doppler Decomposition for Robust Wi-Fi-based Human Activity Recognition—0
AgentSense: Virtual Sensor Data Generation Using LLM Agents in Simulated Home Environments—0
ScalableHD: Scalable and High-Throughput Hyperdimensional Computing Inference on Multi-Core CPUs—0
Scaling Human Activity Recognition: A Comparative Evaluation of Synthetic Data Generation and Augmentation Techniques—0
Through-the-Wall Radar Human Activity Recognition WITHOUT Using Neural NetworksCode0
Spatiotemporal Analysis of Forest Machine Operations Using 3D Video Classification—0
Knowledge Distillation for Reservoir-based Classifier: Human Activity Recognition—0
Predicting Human Depression with Hybrid Data Acquisition utilizing Physical Activity Sensing and Social Media Feeds—0
A Probabilistic Jump-Diffusion Framework for Open-World Egocentric Activity Recognition—0
MAC-Gaze: Motion-Aware Continual Calibration for Mobile Gaze Tracking—0
DeepConvContext: A Multi-Scale Approach to Timeseries Classification in Human Activity RecognitionCode0
Recognition of Physiological Patterns during Activities of Daily Living Using Wearable Biosignal Sensors—0
Label Leakage in Federated Inertial-based Human Activity RecognitionCode0
Enhancing Wearable Tap Water Audio Detection through Subclass Annotation in the HD-Epic DatasetCode0
MoPFormer: Motion-Primitive Transformer for Wearable-Sensor Activity Recognition—0
CA3D: Convolutional-Attentional 3D Nets for Efficient Video Activity Recognition on the Edge—0
SETransformer: A Hybrid Attention-Based Architecture for Robust Human Activity Recognition—0
PosePilot: An Edge-AI Solution for Posture Correction in Physical Exercises—0
Few-Shot Optimization for Sensor Data Using Large Language Models: A Case Study on Fatigue Detection—0
BiomechGPT: Towards a Biomechanically Fluent Multimodal Foundation Model for Clinically Relevant Motion Tasks—0
SPAR: Self-supervised Placement-Aware Representation Learning for Multi-Node IoT Systems—0
Show:102550
← PrevPage 1 of 53Next →
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