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 1176–1200 of 1322 papers

TitleStatusHype
Boosted Markov Networks for Activity Recognition—0
Boosted Multiple Kernel Learning for First-Person Activity Recognition—0
Boosting Adversarial Transferability for Skeleton-based Action Recognition via Exploring the Model Posterior Space—0
BSDGAN: Balancing Sensor Data Generative Adversarial Networks for Human Activity Recognition—0
Bubblenet: A Disperse Recurrent Structure To Recognize Activities—0
CA3D: Convolutional-Attentional 3D Nets for Efficient Video Activity Recognition on the Edge—0
CADDI: An in-Class Activity Detection Dataset using IMU data from low-cost sensors—0
CamLoc: Pedestrian Location Detection from Pose Estimation on Resource-constrained Smart-cameras—0
Can a simple approach identify complex nurse care activity?—0
Can Ensemble of Classifiers Provide Better Recognition Results in Packaging Activity?—0
Can You Spot the Semantic Predicate in this Video?—0
CDFL: Efficient Federated Human Activity Recognition using Contrastive Learning and Deep Clustering—0
CERN: Confidence-Energy Recurrent Network for Group Activity Recognition—0
cGAN-Based High Dimensional IMU Sensor Data Generation for Enhanced Human Activity Recognition in Therapeutic Activities—0
Channel Phase Processing in Wireless Networks for Human Activity Recognition—0
CHARM: A Hierarchical Deep Learning Model for Classification of Complex Human Activities Using Motion Sensors—0
Cheating off your neighbors: Improving activity recognition through corroboration—0
CKSP: Cross-species Knowledge Sharing and Preserving for Universal Animal Activity Recognition—0
CLAD: A Complex and Long Activities Dataset with Rich Crowdsourced Annotations—0
Self-supervised New Activity Detection in Sensor-based Smart Environments—0
Classification of grasping tasks based on EEG-EMG coherence—0
Classification of human activity recognition using smartphones—0
Classifying Human Activities using Machine Learning and Deep Learning Techniques—0
Classifying Human Activities with Inertial Sensors: A Machine Learning Approach—0
CMD-HAR: Cross-Modal Disentanglement for Wearable Human Activity Recognition—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