Activity Recognition
254 papers with code • 4 benchmarks • 29 datasets
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
Benchmarks
These leaderboards are used to track progress in Activity Recognition
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Libraries
Use these libraries to find Activity Recognition models and implementationsDatasets
GazeFollow
MotionSense
Drive&Act
RWF-2000
First-Person Hand Action Benchmark
MoVi
MSR ActionPairs
Home Action Genome
Subtasks
Latest papers with no code
P2LHAP:Wearable sensor-based human activity recognition, segmentation and forecast through Patch-to-Label Seq2Seq Transformer
Traditional deep learning methods struggle to simultaneously segment, recognize, and forecast human activities from sensor data.
Knowledge Transfer across Multiple Principal Component Analysis Studies
In the first step, we integrate the shared subspace information across multiple studies by a proposed method named as Grassmannian barycenter, instead of directly performing PCA on the pooled dataset.
Deep Generative Domain Adaptation with Temporal Relation Knowledge for Cross-User Activity Recognition
To bridge this gap, our study introduces a Conditional Variational Autoencoder with Universal Sequence Mapping (CVAE-USM) approach, which addresses the unique challenges of time-series domain adaptation in HAR by relaxing the i. i. d.
Cross-user activity recognition using deep domain adaptation with temporal relation information
To address this challenge, we introduce the Deep Temporal State Domain Adaptation (DTSDA) model, an innovative approach tailored for time series domain adaptation in cross-user HAR.
Cross-user activity recognition via temporal relation optimal transport
$ and do not consider the knowledge of temporal relation hidden in time series data for aligning data distribution.
Machine Learning Techniques for Sensor-based Human Activity Recognition with Data Heterogeneity -- A Review
Sensor-based Human Activity Recognition (HAR) is crucial in ubiquitous computing, analysing behaviours through multi-dimensional observations.
Deep Generative Domain Adaptation with Temporal Attention for Cross-User Activity Recognition
Addressing this oversight, our research presents the Deep Generative Domain Adaptation with Temporal Attention (DGDATA) method.
ContextGPT: Infusing LLMs Knowledge into Neuro-Symbolic Activity Recognition Models
Neuro-Symbolic AI (NeSy) provides an interesting research direction to mitigate this issue, by infusing common-sense knowledge about human activities and the contexts in which they can be performed into HAR deep learning classifiers.
FocusCLIP: Multimodal Subject-Level Guidance for Zero-Shot Transfer in Human-Centric Tasks
We propose FocusCLIP, integrating subject-level guidance--a specialized mechanism for target-specific supervision--into the CLIP framework for improved zero-shot transfer on human-centric tasks.
A Survey of Application of Machine Learning in Wireless Indoor Positioning Systems
Numerous attempts have been made in the literature to develop efficient indoor positioning systems (IPSs), with a growing focus on machine learning (ML) based techniques.
