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 1151–1200 of 1322 papers

TitleStatusHype
Automatic Operating Room Surgical Activity Recognition for Robot-Assisted Surgery—0
AVD: Adversarial Video Distillation—0
A Wearable Multi-Modal Edge-Computing System for Real-Time Kitchen Activity Recognition—0
A Wi-Fi Signal-Based Human Activity Recognition Using High-Dimensional Factor Models—0
A Wireless-Vision Dataset for Privacy Preserving Human Activity Recognition—0
Background Knowledge Injection for Interpretable Sequence Classification—0
Balancing Continual Learning and Fine-tuning for Human Activity Recognition—0
Balancing Privacy and Action Performance: A Penalty-Driven Approach to Image Anonymization—0
BAR: Bayesian Activity Recognition using variational inference—0
Batch-Based Activity Recognition from Egocentric Photo-Streams—0
Benchmarking 2D Egocentric Hand Pose Datasets—0
Benchmarking Classical, Deep, and Generative Models for Human Activity Recognition—0
Benchmark of DNN Model Search at Deployment Time—0
Beyond Actions: Discriminative Models for Contextual Group Activities—0
Beyond Confusion: A Fine-grained Dialectical Examination of Human Activity Recognition Benchmark Datasets—0
Beyond Isolated Frames: Enhancing Sensor-Based Human Activity Recognition through Intra- and Inter-Frame Attention—0
Beyond the Gates of Euclidean Space: Temporal-Discrimination-Fusions and Attention-based Graph Neural Network for Human Activity Recognition—0
Bi-Causal: Group Activity Recognition via Bidirectional Causality—0
Bilinear Programming for Human Activity Recognition with Unknown MRF Graphs—0
Bi-LSTM neural network for EEG-based error detection in musicians' performance—0
BiomechGPT: Towards a Biomechanically Fluent Multimodal Foundation Model for Clinically Relevant Motion Tasks—0
Black-box Attacks on Image Activity Prediction and its Natural Language Explanations—0
Body-Area Capacitive or Electric Field Sensing for Human Activity Recognition and Human-Computer Interaction: A Comprehensive Survey—0
BON: An extended public domain dataset for human activity recognition—0
Bonn Activity Maps: Dataset Description—0
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