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 876–900 of 1322 papers

TitleStatusHype
ARC-Net: Activity Recognition Through Capsules—0
Continual Learning in Human Activity Recognition: an Empirical Analysis of Regularization—0
Joint Learning of Social Groups, Individuals Action and Sub-group Activities in Videos—0
Handling Variable-Dimensional Time Series with Graph Neural Networks—0
Human Activity Recognition based on Dynamic Spatio-Temporal Relations—0
Automatic Operating Room Surgical Activity Recognition for Robot-Assisted Surgery—0
DanHAR: Dual Attention Network For Multimodal Human Activity Recognition Using Wearable Sensors—0
Background Knowledge Injection for Interpretable Sequence Classification—0
A dataset for complex activity recognition withmicro and macro activities in a cooking scenario—0
Learning-to-Learn Personalised Human Activity Recognition Models—0
AdaSense: Adaptive Low-Power Sensing and Activity Recognition for Wearable Devices—0
On Matched Filtering for Statistical Change Point Detection—0
Attention-Based Deep Learning Framework for Human Activity Recognition with User AdaptationCode1
Real-time Human Activity Recognition Using Conditionally Parametrized Convolutions on Mobile and Wearable Devices—0
Entropy Decision Fusion for Smartphone Sensor based Human Activity Recognition—0
IMUTube: Automatic Extraction of Virtual on-body Accelerometry from Video for Human Activity Recognition—0
Incremental Real-Time Personalization in Human Activity Recognition Using Domain Adaptive Batch Normalization—0
Toward Automated Classroom Observation: Multimodal Machine Learning to Estimate CLASS Positive Climate and Negative Climate—0
Sensor Data for Human Activity Recognition: Feature Representation and Benchmarking—0
Enabling Edge Cloud Intelligence for Activity Learning in Smart Home—0
Utility-aware Privacy-preserving Data Releasing—0
Layer-wise training convolutional neural networks with smaller filters for human activity recognition using wearable sensors—0
Approaches and Applications of Early Classification of Time Series: A Review—0
Don't Explain without Verifying Veracity: An Evaluation of Explainable AI with Video Activity Recognition—0
SleepPoseNet: Multi-View Learning for Sleep Postural Transition Recognition Using UWBCode1
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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