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 226–250 of 1322 papers

TitleStatusHype
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
Activity Modeling in Smart Home using High Utility Pattern Mining over Data Streams—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
An Integrated Approach to Crowd Video Analysis: From Tracking to Multi-level Activity Recognition—0
Compact CNN for Indexing Egocentric Videos—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
Attention-Based Sensor Fusion for Human Activity Recognition Using IMU Signals—0
Channel Phase Processing in Wireless Networks for Human Activity Recognition—0
A Hybrid Framework for Action Recognition in Low-Quality Video Sequences—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