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 451–475 of 1322 papers

TitleStatusHype
Empowering Relational Network by Self-Attention Augmented Conditional Random Fields for Group Activity Recognition—0
Enabling Edge Cloud Intelligence for Activity Learning in Smart Home—0
Enabling Machine Learning Across Heterogeneous Sensor Networks with Graph Autoencoders—0
Encoding Based Saliency Detection for Videos and Images—0
Energy Expenditure Estimation Through Daily Activity Recognition Using a Smart-phone—0
Enforcing Fundamental Relations via Adversarial Attacks on Input Parameter Correlations—0
Body-Area Capacitive or Electric Field Sensing for Human Activity Recognition and Human-Computer Interaction: A Comprehensive Survey—0
An Efficient Data Imputation Technique for Human Activity Recognition—0
Enhancing Smart Environments with Context-Aware Chatbots using Large Language Models—0
BON: An extended public domain dataset for human activity recognition—0
EnHDC: Ensemble Learning for Brain-Inspired Hyperdimensional Computing—0
Bonn Activity Maps: Dataset Description—0
Ensembles of Deep LSTM Learners for Activity Recognition using Wearables—0
Entropy Decision Fusion for Smartphone Sensor based Human Activity Recognition—0
Are Accelerometers for Activity Recognition a Dead-end?—0
ESPARGOS: An Ultra Low-Cost, Realtime-Capable Multi-Antenna WiFi Channel Sounder—0
Babel: A Scalable Pre-trained Model for Multi-Modal Sensing via Expandable Modality Alignment—0
Estimating Human Poses Across Datasets: A Unified Skeleton and Multi-Teacher Distillation Approach—0
Explaining Motion Relevance for Activity Recognition in Video Deep Learning Models—0
Evaluating Deep Neural Network Ensembles by Majority Voting cum Meta-Learning scheme—0
BSDGAN: Balancing Sensor Data Generative Adversarial Networks for Human Activity Recognition—0
Evaluation and comparison of federated learning algorithms for Human Activity Recognition on smartphones—0
Evaluation of Encoding Schemes on Ubiquitous Sensor Signal for Spiking Neural Network—0
Evaluation of Regularization-based Continual Learning Approaches: Application to HAR—0
Contrastive Predictive Coding 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