Activity Recognition
253 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
MISAW
Subtasks
Latest papers with no code
Meta-Decomposition: Dynamic Segmentation Approach Selection in IoT-based Activity Recognition
Therefore, It leads to an improvement in the evaluation process and, consequently, in the selection of the appropriate segmentation method.
Design and Analysis of Efficient Attention in Transformers for Social Group Activity Recognition
Social group activity recognition is a challenging task extended from group activity recognition, where social groups must be recognized with their activities and group members.
A Transformer-Based Model for the Prediction of Human Gaze Behavior on Videos
Eye-tracking applications that utilize the human gaze in video understanding tasks have become increasingly important.
Generative Resident Separation and Multi-label Classification for Multi-person Activity Recognition
This paper presents two models to address the problem of multi-person activity recognition using ambient sensors in a home.
Two-Person Interaction Augmentation with Skeleton Priors
Close and continuous interaction with rich contacts is a crucial aspect of human activities (e. g. hugging, dancing) and of interest in many domains like activity recognition, motion prediction, character animation, etc.
Human Activity Recognition using Smartphones
In our project, we have created an Android application that recognizes the daily human activities and calculate the calories burnt in real time.
EventSleep: Sleep Activity Recognition with Event Cameras
Our novel pipeline is able to achieve high accuracy under these challenging conditions and incorporates a Bayesian approach (Laplace ensembles) to increase the robustness in the predictions, which is fundamental for medical applications.
HARMamba: Efficient Wearable Sensor Human Activity Recognition Based on Bidirectional Selective SSM
Recently, an efficient hardware-aware state space model (SSM) called Mamba has emerged as a promising alternative.
Emotion Recognition from the perspective of Activity Recognition
In this paper, we treat emotion recognition from the perspective of action recognition by exploring the application of deep learning architectures specifically designed for action recognition, for continuous affect recognition.
CODA: A COst-efficient Test-time Domain Adaptation Mechanism for HAR
In recent years, emerging research on mobile sensing has led to novel scenarios that enhance daily life for humans, but dynamic usage conditions often result in performance degradation when systems are deployed in real-world settings.
