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 1076–1100 of 1322 papers

TitleStatusHype
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