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 426–450 of 1322 papers

TitleStatusHype
A Matter of Annotation: An Empirical Study on In Situ and Self-Recall Activity Annotations from Wearable SensorsCode0
Is end-to-end learning enough for fitness activity recognition?—0
Group Activity Recognition via Dynamic Composition and Interaction—0
Distilled Mid-Fusion Transformer Networks for Multi-Modal Human Activity Recognition—0
Generating Virtual On-body Accelerometer Data from Virtual Textual Descriptions for Human Activity RecognitionCode1
SoGAR: Self-supervised Spatiotemporal Attention-based Social Group Activity Recognition—0
Human Activity Recognition Using Self-Supervised Representations of Wearable Data—0
Evaluation of Regularization-based Continual Learning Approaches: Application to HAR—0
RHM: Robot House Multi-view Human Activity Recognition DatasetCode0
A Survey on Multi-Resident Activity Recognition in Smart Environments—0
Fruit Picker Activity Recognition with Wearable Sensors and Machine Learning—0
Big-Little Adaptive Neural Networks on Low-Power Near-Subthreshold ProcessorsCode0
Automatic Interaction and Activity Recognition from Videos of Human Manual Demonstrations with Application to Anomaly Detection—0
SelfAct: Personalized Activity Recognition based on Self-Supervised and Active Learning—0
Human activity recognition using deep learning approaches and single frame cnn and convolutional lstm—0
MLP-AIR: An Efficient MLP-Based Method for Actor Interaction Relation Learning in Group Activity Recognition—0
Contactless Human Activity Recognition using Deep Learning with Flexible and Scalable Software Define Radio—0
Applications of Deep Learning for Top-View Omnidirectional Imaging: A Survey—0
Explaining, Analyzing, and Probing Representations of Self-Supervised Learning Models for Sensor-based Human Activity Recognition—0
Continuous Human Activity Recognition using a MIMO Radar for Transitional Motion Analysis—0
Domain Adaptation for Inertial Measurement Unit-based Human Activity Recognition: A Survey—0
VicTR: Video-conditioned Text Representations for Activity Recognition—0
Multi-Channel Time-Series Person and Soft-Biometric Identification—0
WSense: A Robust Feature Learning Module for Lightweight Human Activity RecognitionCode0
Channel Phase Processing in Wireless Networks for 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