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 176–200 of 1322 papers

TitleStatusHype
ActivityNet Challenge 2017 Summary—0
Augmenting Vision-Based Human Pose Estimation with Rotation Matrix—0
Model enhancement and personalization using weakly supervised learning for multi-modal mobile sensing—0
BON: An extended public domain dataset for human activity recognition—0
Boosted Markov Networks for Activity Recognition—0
Boosting Adversarial Transferability for Skeleton-based Action Recognition via Exploring the Model Posterior Space—0
CamLoc: Pedestrian Location Detection from Pose Estimation on Resource-constrained Smart-cameras—0
A Logic Programming Approach to Activity Recognition—0
Activity Monitoring of Islamic Prayer (Salat) Postures using Deep Learning—0
A comparative study on wearables and single-camera video for upper-limb out-of-thelab activity recognition with different deep learning architectures—0
Bilinear Programming for Human Activity Recognition with Unknown MRF Graphs—0
Attributes for Improved Attributes: A Multi-Task Network for Attribute Classification—0
Attentive pooling for Group Activity Recognition—0
Augmenting Bag-of-Words: Data-Driven Discovery of Temporal and Structural Information for Activity Recognition—0
Augmenting Deep Learning Adaptation for Wearable Sensor Data through Combined Temporal-Frequency Image Encoding—0
A Lightweight Deep Learning Model for Human Activity Recognition on Edge Devices—0
AI-Powered Non-Contact In-Home Gait Monitoring and Activity Recognition System Based on mm-Wave FMCW Radar and Cloud Computing—0
A Masked Semi-Supervised Learning Approach for Otago Micro Labels Recognition—0
Automated Activity Recognition in Clinical Documents—0
Automated Activity Recognition of Construction Equipment Using a Data Fusion Approach—0
Automated Human Activity Recognition by Colliding Bodies Optimization-based Optimal Feature Selection with Recurrent Neural Network—0
Automated Level Crossing System: A Computer Vision Based Approach with Raspberry Pi Microcontroller—0
Automated Surgical Activity Recognition with One Labeled Sequence—0
WearableMil: An End-to-End Framework for Military Activity Recognition and Performance Monitoring—0
Attention-Driven Body Pose Encoding for Human Activity Recognition—0
Show:102550
← PrevPage 8 of 53Next →
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