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 926–950 of 1322 papers

TitleStatusHype
Manipulated Object Proposal: A Discriminative Object Extraction and Feature Fusion Framework for First-Person Daily Activity Recognition—0
MARS: Mixed Virtual and Real Wearable Sensors for Human Activity Recognition with Multi-Domain Deep Learning Model—0
Activity Recognition on Avatar-Anonymized Datasets with Masked Differential Privacy—0
MaskFi: Unsupervised Learning of WiFi and Vision Representations for Multimodal Human Activity Recognition—0
Measuring the Utilization of Public Open Spaces by Deep Learning: a Benchmark Study at the Detroit Riverfront—0
MESEN: Exploit Multimodal Data to Design Unimodal Human Activity Recognition with Few Labels—0
Meta-Decomposition: Dynamic Segmentation Approach Selection in IoT-based Activity Recognition—0
Meta-Learning for Few-Shot Time Series Classification—0
Metric-based multimodal meta-learning for human movement identification via footstep recognition—0
MEVA: A Large-Scale Multiview, Multimodal Video Dataset for Activity Detection—0
MIcro-Surgical Anastomose Workflow recognition challenge report—0
Millimeter Wave Radar-based Human Activity Recognition for Healthcare Monitoring Robot—0
Mitigating Class Boundary Label Uncertainty to Reduce Both Model Bias and Variance—0
MLP-AIR: An Efficient MLP-Based Method for Actor Interaction Relation Learning in Group Activity Recognition—0
mmID: High-Resolution mmWave Imaging for Human Identification—0
Mobile Big Data Analytics Using Deep Learning and Apache Spark—0
Mobiprox: Supporting Dynamic Approximate Computing on Mobiles—0
A Light-weight Deep Human Activity Recognition Algorithm Using Multi-knowledge Distillation—0
Unleashing the Power of Shared Label Structures for Human Activity Recognition—0
Modeling the Trade-off of Privacy Preservation and Activity Recognition on Low-Resolution Images—0
Modelling the Influence of Cultural Information on Vision-Based Human Home Activity Recognition—0
ModSelect: Automatic Modality Selection for Synthetic-to-Real Domain Generalization—0
MoPFormer: Motion-Primitive Transformer for Wearable-Sensor Activity Recognition—0
MORIC: CSI Delay-Doppler Decomposition for Robust Wi-Fi-based Human Activity Recognition—0
Motion Classification using Kinematically Sifted ACGAN-Synthesized Radar Micro-Doppler Signatures—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