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 1126–1150 of 1322 papers

TitleStatusHype
A Survey on Multimodal Wearable Sensor-based Human Action Recognition—0
A Survey on Multi-Resident Activity Recognition in Smart Environments—0
A Symbolic Representation of Human Posture for Interpretable Learning and Reasoning—0
AsyMov: Integrated Sensing and Communications with Asynchronous Moving Devices—0
A systematic review of smartphone-based human activity recognition for health research—0
A Tiny Supervised ODL Core with Auto Data Pruning for Human Activity Recognition—0
A Transfer Learning Method for Goal Recognition Exploiting Cross-Domain Spatial Features—0
A Transformer-Based Model for the Prediction of Human Gaze Behavior on Videos—0
A Tree-structure Convolutional Neural Network for Temporal Features Exaction on Sensor-based Multi-resident Activity Recognition—0
Attend And Discriminate: Beyond the State-of-the-Art for Human Activity Recognition using Wearable Sensors—0
Attention-based Convolutional Neural Network for Weakly Labeled Human Activities Recognition with Wearable Sensors—0
Attention-Based Sensor Fusion for Human Activity Recognition Using IMU Signals—0
Attention-Driven Body Pose Encoding for Human Activity Recognition—0
Attentive pooling for Group Activity Recognition—0
Attributes for Improved Attributes: A Multi-Task Network for Attribute Classification—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
Augmenting Vision-Based Human Pose Estimation with Rotation Matrix—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
Automatic Interaction and Activity Recognition from Videos of Human Manual Demonstrations with Application to Anomaly Detection—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