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 1051–1100 of 1322 papers

TitleStatusHype
Babel: A Scalable Pre-trained Model for Multi-Modal Sensing via Expandable Modality Alignment—0
Adversarial Deep Feature Extraction Network for User Independent Human Activity Recognition—0
Adversarial Transferability in Wearable Sensor Systems—0
A Feature Selection Method for Multi-Dimension Time-Series Data—0
Affinity-Based Hierarchical Learning of Dependent Concepts for Human Activity Recognition—0
A Fourier Domain Feature Approach for Human Activity Recognition & Fall Detection—0
A Framework For Identifying Group Behavior Of Wild Animals—0
A framework for mining process models from emails logs—0
AgentSense: Virtual Sensor Data Generation Using LLM Agents in Simulated Home Environments—0
AHAR: Adaptive CNN for Energy-efficient Human Activity Recognition in Low-power Edge Devices—0
A Heat-Map-based Algorithm for Recognizing Group Activities in Videos—0
A Hybrid Framework for Action Recognition in Low-Quality Video Sequences—0
AI-Powered Non-Contact In-Home Gait Monitoring and Activity Recognition System Based on mm-Wave FMCW Radar and Cloud Computing—0
A Lightweight Deep Learning Model for Human Activity Recognition on Edge Devices—0
A Logic Programming Approach to Activity Recognition—0
A Masked Semi-Supervised Learning Approach for Otago Micro Labels Recognition—0
Maximum Likelihood Speed Estimation of Moving Objects in Video Signals—0
"Filling the Blanks'': Identifying Micro-activities that Compose Complex Human Activities of Daily Living—0
Am I fit for this physical activity? Neural embedding of physical conditioning from inertial sensors—0
A MIMO Radar-Based Metric Learning Approach for Activity Recognition—0
A Multi-Modal Explainability Approach for Human-Aware Robots in Multi-Party Conversation—0
A Multi-Stream Convolutional Neural Network Framework for Group Activity Recognition—0
A Multi-Task Deep Learning Approach for Sensor-based Human Activity Recognition and Segmentation—0
An Activity Recognition Framework for Continuous Monitoring of Non-Steady-State Locomotion of Individuals with Parkinson's Disease—0
An Actor-Centric Causality Graph for Asynchronous Temporal Inference in Group Activity—0
An adaptable cognitive microcontroller node for fitness activity recognition—0
Analysis of Gait Pattern to Recognize the Human Activities—0
Analyzing and Exploiting NARX Recurrent Neural Networks for Long-Term Dependencies—0
An Efficient Data Imputation Technique for Human Activity Recognition—0
An end-to-end (deep) neural network applied to raw EEG, fNIRs and body motion data for data fusion and BCI classification task without any pre-/post-processing—0
A Neurorobotics Approach to Behaviour Selection based on Human Activity Recognition—0
An Event Calculus Production Rule System for Reasoning in Dynamic and Uncertain Domains—0
A new network-based algorithm for human activity recognition in video—0
An Information-rich Sampling Technique over Spatio-Temporal CNN for Classification of Human Actions in Videos—0
An Integrated Approach to Crowd Video Analysis: From Tracking to Multi-level Activity Recognition—0
An Intelligent Non-Invasive Real Time Human Activity Recognition System for Next-Generation Healthcare—0
An Interpretable Machine Vision Approach to Human Activity Recognition using Photoplethysmograph Sensor Data—0
An Interval-Based Bayesian Generative Model for Human Complex Activity Recognition—0
Anomaly detection and regime searching in fitness-tracker data—0
Anomaly Recognition from surveillance videos using 3D Convolutional Neural Networks—0
Anonymizing Egocentric Videos—0
A Novel Indoor Positioning System for unprepared firefighting scenarios—0
A Novel Multi-Stage Training Approach for Human Activity Recognition from Multimodal Wearable Sensor Data Using Deep Neural Network—0
A Novel Two Stream Decision Level Fusion of Vision and Inertial Sensors Data for Automatic Multimodal Human Activity Recognition System—0
An Overview of Human Activity Recognition Using Wearable Sensors: Healthcare and Artificial Intelligence—0
An Overview of Violence Detection Techniques: Current Challenges and Future Directions—0
Answering Image Riddles using Vision and Reasoning through Probabilistic Soft Logic—0
An Unsupervised Approach for Automatic Activity Recognition based on Hidden Markov Model Regression—0
Appearance Based Robot and Human Activity Recognition System—0
Application of Machine Learning Techniques in 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