Activity Recognition
246 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
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Use these libraries to find Activity Recognition models and implementationsDatasets
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Latest papers
Activity-Biometrics: Person Identification from Daily Activities
Furthermore, we extensively compare ABNet with existing works in person identification and demonstrate its effectiveness for activity-based biometrics across all five datasets.
NeuFlow: Real-time, High-accuracy Optical Flow Estimation on Robots Using Edge Devices
Given the features of the input images extracted at different spatial resolutions, global matching is employed to estimate an initial optical flow on the 1/16 resolution, capturing large displacement, which is then refined on the 1/8 resolution with lightweight CNN layers for better accuracy.
DiTMoS: Delving into Diverse Tiny-Model Selection on Microcontrollers
Enabling efficient and accurate deep neural network (DNN) inference on microcontrollers is non-trivial due to the constrained on-chip resources.
Class-incremental Learning for Time Series: Benchmark and Evaluation
Real-world environments are inherently non-stationary, frequently introducing new classes over time.
OSSAR: Towards Open-Set Surgical Activity Recognition in Robot-assisted Surgery
Existing algorithms dedicated to surgical activity recognition predominantly cater to pre-defined closed-set paradigms, ignoring the challenges of real-world open-set scenarios.
AutoGCN -- Towards Generic Human Activity Recognition with Neural Architecture Search
This paper introduces AutoGCN, a generic Neural Architecture Search (NAS) algorithm for Human Activity Recognition (HAR) using Graph Convolution Networks (GCNs).
IMUGPT 2.0: Language-Based Cross Modality Transfer for Sensor-Based Human Activity Recognition
With the emergence of generative AI models such as large language models (LLMs) and text-driven motion synthesis models, language has become a promising source data modality as well as shown in proof of concepts such as IMUGPT.
MIFI: MultI-camera Feature Integration for Roust 3D Distracted Driver Activity Recognition
Distracted driver activity recognition plays a critical role in risk aversion-particularly beneficial in intelligent transportation systems.
WiMANS: A Benchmark Dataset for WiFi-based Multi-user Activity Sensing
WiFi-based human sensing has exhibited remarkable potential to analyze user behaviors in a non-intrusive and device-free manner, benefiting applications as diverse as smart homes and healthcare.
A Review of Deep Learning Methods for Photoplethysmography Data
In this review, we systematically reviewed papers that applied deep learning models to process PPG data between January 1st of 2017 and July 31st of 2023 from Google Scholar, PubMed and Dimensions.