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 526–550 of 1322 papers

TitleStatusHype
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