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 501–550 of 1322 papers

TitleStatusHype
Sharing Leaky-Integrate-and-Fire Neurons for Memory-Efficient Spiking Neural Networks—0
Generalizable Low-Resource Activity Recognition with Diverse and Discriminative Representation Learning—0
Large Language Models are Few-Shot Health Learners—0
CSI-Based Efficient Self-Quarantine Monitoring System Using Branchy Convolution Neural Network—0
ConvBoost: Boosting ConvNets for Sensor-based Activity RecognitionCode0
Hang-Time HAR: A Benchmark Dataset for Basketball Activity Recognition using Wrist-Worn Inertial SensorsCode0
FieldHAR: A Fully Integrated End-to-end RTL Framework for Human Activity Recognition with Neural Networks from Heterogeneous Sensors—0
Real-time Aerial Detection and Reasoning on Embedded-UAVs—0
WiFi-TCN: Temporal Convolution for Human Interaction Recognition based on WiFi signal—0
Privacy in Multimodal Federated Human Activity Recognition—0
Smart Pressure e-Mat for Human Sleeping Posture and Dynamic Activity Recognition—0
rWISDM: Repaired WISDM, a Public Dataset for Human Activity Recognition—0
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
SoGAR: Self-supervised Spatiotemporal Attention-based Social Group Activity Recognition—0
Evaluation of Regularization-based Continual Learning Approaches: Application to HAR—0
Human Activity Recognition Using Self-Supervised Representations of Wearable Data—0
A Survey on Multi-Resident Activity Recognition in Smart Environments—0
RHM: Robot House Multi-view Human Activity Recognition DatasetCode0
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
MLP-AIR: An Efficient MLP-Based Method for Actor Interaction Relation Learning in Group Activity Recognition—0
Human activity recognition using deep learning approaches and single frame cnn and convolutional lstm—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
Hard Regularization to Prevent Deep Online Clustering Collapse without Data AugmentationCode0
Sensing with OFDM Waveform at mmWave Band based on Micro-Doppler Analysis—0
Provable Robustness for Streaming Models with a Sliding Window—0
Unified Keypoint-based Action Recognition Framework via Structured Keypoint Pooling—0
Learning and Verification of Task Structure in Instructional Videos—0
A Multi-Task Deep Learning Approach for Sensor-based Human Activity Recognition and Segmentation—0
Modeling the Trade-off of Privacy Preservation and Activity Recognition on Low-Resolution Images—0
Mobiprox: Supporting Dynamic Approximate Computing on Mobiles—0
Activity Recognition From Newborn Resuscitation Videos—0
DECOMPL: Decompositional Learning with Attention Pooling for Group Activity Recognition from a Single Volleyball ImageCode0
Zone-based Federated Learning for Mobile Sensing Data—0
Robust Multimodal Fusion for Human Activity Recognition—0
Sleep Quality Prediction from Wearables using Convolution Neural Networks and Ensemble Learning—0
SPARTAN: Self-supervised Spatiotemporal Transformers Approach to Group Activity RecognitionCode0
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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