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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 676–700 of 1322 papers

TitleStatusHype
Time Series Similarity Score Functions to Monitor and Interact with the Training and Denoising Process of a Time Series Diffusion Model applied to a Human Activity Recognition Dataset based on IMUs—0
Timestamp-supervised Wearable-based Activity Segmentation and Recognition with Contrastive Learning and Order-Preserving Optimal Transport—0
Too Good To Be True: performance overestimation in (re)current practices for Human Activity Recognition—0
Topological Persistence Guided Knowledge Distillation for Wearable Sensor Data—0
Toward Automated Classroom Observation: Multimodal Machine Learning to Estimate CLASS Positive Climate and Negative Climate—0
Towards Battery-Free Wireless Sensing via Radio-Frequency Energy Harvesting—0
Towards Child-Inclusive Clinical Video Understanding for Autism Spectrum Disorder—0
Towards Deep Clustering of Human Activities from Wearables—0
Towards Generalizable Surgical Activity Recognition Using Spatial Temporal Graph Convolutional Networks—0
Towards Learning Discrete Representations via Self-Supervision for Wearables-Based Human Activity Recognition—0
Towards LLM-Powered Ambient Sensor Based Multi-Person Human Activity Recognition—0
Towards Robust Human Activity Recognition from RGB Video Stream with Limited Labeled Data—0
Towards Stroke Patients' Upper-limb Automatic Motor Assessment Using Smartwatches—0
Towards Sustainable Personalized On-Device Human Activity Recognition with TinyML and Cloud-Enabled Auto Deployment—0
Towards Using Unlabeled Data in a Sparse-coding Framework for Human Activity Recognition—0
Toyota Smarthome: Real-World Activities of Daily Living—0
Transfer Learning for Future Wireless Networks: A Comprehensive Survey—0
Transfer Learning for Human Activity Recognition using Representational Analysis of Neural Networks—0
Transfer Learning in a Transductive Setting—0
Transfer Learning in Human Activity Recognition: A Survey—0
Transformer-Based Approaches for Sensor-Based Human Activity Recognition: Opportunities and Challenges—0
Transformer-Based Contrastive Meta-Learning For Low-Resource Generalizable Activity Recognition—0
Transformers in Vision: A Survey—0
Transportation mode recognition based on low-rate acceleration and location signals with an attention-based multiple-instance learning network—0
Tri-axial Self-Attention for Concurrent 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