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
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.
HARGPT: Are LLMs Zero-Shot Human Activity Recognizers?
Our study, HARGPT, presents an affirmative answer by demonstrating that LLMs can comprehend raw IMU data and perform HAR tasks in a zero-shot manner, with only appropriate prompts.
Human Activity Recognition with Low-Resolution Infrared Array Sensor Using Semi-supervised Cross-domain Neural Networks for Indoor Environment
The label classifier obtained from training the source domain data improves the recognition of target domain activities due to the semi-supervised learning utilized in training the target domain data.
Fast Low-parameter Video Activity Localization in Collaborative Learning Environments
Research on video activity detection has primarily focused on identifying well-defined human activities in short video segments.
MaskFi: Unsupervised Learning of WiFi and Vision Representations for Multimodal Human Activity Recognition
Benefiting from our unsupervised learning procedure, the network requires only a small amount of annotated data for finetuning and can adapt to the new environment with better performance.
