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 326–350 of 1322 papers

TitleStatusHype
Representation Flow for Action RecognitionCode0
Anomaly Recognition from surveillance videos using 3D Convolutional Neural Networks—0
Cheating off your neighbors: Improving activity recognition through corroboration—0
Anomaly detection and regime searching in fitness-tracker data—0
Adaptive Activity Monitoring with Uncertainty Quantification in Switching Gaussian Process Models—0
CHARM: A Hierarchical Deep Learning Model for Classification of Complex Human Activities Using Motion Sensors—0
Channel Phase Processing in Wireless Networks for Human Activity Recognition—0
cGAN-Based High Dimensional IMU Sensor Data Generation for Enhanced Human Activity Recognition in Therapeutic Activities—0
An Interval-Based Bayesian Generative Model for Human Complex Activity Recognition—0
Adaptation of Surgical Activity Recognition Models Across Operating Rooms—0
ActiLabel: A Combinatorial Transfer Learning Framework for Activity Recognition—0
CERN: Confidence-Energy Recurrent Network for Group Activity Recognition—0
CDFL: Efficient Federated Human Activity Recognition using Contrastive Learning and Deep Clustering—0
An Interpretable Machine Vision Approach to Human Activity Recognition using Photoplethysmograph Sensor Data—0
Can You Spot the Semantic Predicate in this Video?—0
Can Ensemble of Classifiers Provide Better Recognition Results in Packaging Activity?—0
AdaFPP: Adapt-Focused Bi-Propagating Prototype Learning for Panoramic Activity Recognition—0
Can a simple approach identify complex nurse care activity?—0
CamLoc: Pedestrian Location Detection from Pose Estimation on Resource-constrained Smart-cameras—0
An Intelligent Non-Invasive Real Time Human Activity Recognition System for Next-Generation Healthcare—0
An Integrated Approach to Crowd Video Analysis: From Tracking to Multi-level Activity Recognition—0
Actor-Transformers for Group Activity Recognition—0
A Critical Analysis on Machine Learning Techniques for Video-based Human Activity Recognition of Surveillance Systems: A Review—0
CADDI: An in-Class Activity Detection Dataset using IMU data from low-cost sensors—0
CA3D: Convolutional-Attentional 3D Nets for Efficient Video Activity Recognition on the Edge—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