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 826–850 of 1322 papers

TitleStatusHype
Human activity recognition using deep learning approaches and single frame cnn and convolutional lstm—0
Human activity recognition using improved dynamic image—0
Human Activity Recognition using Inertial, Physiological and Environmental Sensors: a Comprehensive Survey—0
Human Activity Recognition Using LSTM-RNN Deep Neural Network Architecture—0
Human Activity Recognition Using Multichannel Convolutional Neural Network—0
Human Activity Recognition using Recurrent Neural Networks—0
Human Activity Recognition Using Robust Adaptive Privileged Probabilistic Learning—0
Human Activity Recognition Using Self-Supervised Representations of Wearable Data—0
Human Activity Recognition using Smartphone—0
Human Activity Recognition using Smartphones—0
Human Activity Recognition Using Tools of Convolutional Neural Networks: A State of the Art Review, Data Sets, Challenges and Future Prospects—0
Human Activity Recognition with a 6.5 GHz Reconfigurable Intelligent Surface for Wi-Fi 6E—0
Human Activity Recognition with Low-Resolution Infrared Array Sensor Using Semi-supervised Cross-domain Neural Networks for Indoor Environment—0
Human Body Parts Tracking: Applications to Activity Recognition—0
Human Interaction Learning on 3D Skeleton Point Clouds for Video Violence Recognition—0
Human Interaction Recognition Framework based on Interacting Body Part Attention—0
Human-like Relational Models for Activity Recognition in Video—0
Human Pose Estimation using Motion Priors and Ensemble Models—0
Hunting Group Clues with Transformers for Social Group Activity Recognition—0
Hybrid Model Featuring CNN and LSTM Architecture for Human Activity Recognition on Smartphone Sensor Data—0
Identifying First-person Camera Wearers in Third-person Videos—0
iKAN: Global Incremental Learning with KAN for Human Activity Recognition Across Heterogeneous Datasets—0
Image based Eye Gaze Tracking and its Applications—0
iMove: Exploring Bio-impedance Sensing for Fitness Activity Recognition—0
Impact of Physical Activity on Sleep:A Deep Learning Based Exploration—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