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 326–350 of 1322 papers

TitleStatusHype
Dataiku's Solution to SPHERE's Activity Recognition Challenge—0
Dataset Bias in Human Activity Recognition—0
A Close Look into Human Activity Recognition Models using Deep Learning—0
Day2Dark: Pseudo-Supervised Activity Recognition beyond Silent Daylight—0
Decoding Children's Social Behavior—0
Decoding Human Activities: Analyzing Wearable Accelerometer and Gyroscope Data for Activity Recognition—0
A Fourier Domain Feature Approach for Human Activity Recognition & Fall Detection—0
A Survey of IMU Based Cross-Modal Transfer Learning in Human Activity Recognition—0
Decoupled Prompt-Adapter Tuning for Continual Activity Recognition—0
Deep Action- and Context-Aware Sequence Learning for Activity Recognition and Anticipation—0
Deep Activity Recognition Models with Triaxial Accelerometers—0
Deep Adaptive Temporal Pooling for Activity Recognition—0
Deep Adversarial Learning with Activity-Based User Discrimination Task for Human Activity Recognition—0
Deep Auto-Set: A Deep Auto-Encoder-Set Network for Activity Recognition Using Wearables—0
A Survey on Multimodal Wearable Sensor-based Human Action Recognition—0
Deep, Convolutional, and Recurrent Models for Human Activity Recognition using Wearables—0
DeepCount: Crowd Counting with WiFi via Deep Learning—0
Deep Generative Domain Adaptation with Temporal Relation Knowledge for Cross-User Activity Recognition—0
Deep Generative Domain Adaptation with Temporal Attention for Cross-User Activity Recognition—0
AsyMov: Integrated Sensing and Communications with Asynchronous Moving Devices—0
Cross-Country Skiing Gears Classification using Deep Learning—0
Deep Learning for Computer Vision based Activity Recognition and Fall Detection of the Elderly: a Systematic Review—0
A systematic review of smartphone-based human activity recognition for health research—0
Deep Learning for Sensor-based Human Activity Recognition: Overview, Challenges and Opportunities—0
CROMOSim: A Deep Learning-based Cross-modality Inertial Measurement Simulator—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