Object Counting
62 papers with code • 10 benchmarks • 23 datasets
The goal of Object Counting task is to count the number of object instances in a single image or video sequence. It has many real-world applications such as traffic flow monitoring, crowdedness estimation, and product counting.
Source: Learning to Count Objects with Few Exemplar Annotations
Benchmarks
These leaderboards are used to track progress in Object Counting
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Libraries
Use these libraries to find Object Counting models and implementations
MS COCO
PASCAL VOC
PASCAL VOC 2007
FSOD
CARPK
FSC147
COWC
TallyQA
AGAR
Most implemented papers
MoVie: Revisiting Modulated Convolutions for Visual Counting and Beyond
facebookresearch/mmf
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ICLR 2021
This paper focuses on visual counting, which aims to predict the number of occurrences given a natural image and a query (e. g. a question or a category).
Counting from Sky: A Large-scale Dataset for Remote Sensing Object Counting and A Benchmark Method
Object counting, whose aim is to estimate the number of objects from a given image, is an important and challenging computation task.
PSGCNet: A Pyramidal Scale and Global Context Guided Network for Dense Object Counting in Remote Sensing Images
Object counting, which aims to count the accurate number of object instances in images, has been attracting more and more attention.
Heatmap-based Object Detection and Tracking with a Fully Convolutional Neural Network
While CueNet V1 has a single input image, the approach with CueNet V2 was to take three consecutive 240 x 180-pixel images as an input and transform them into a probability heatmap for the cueball's location.
Image Augmentation for Multitask Few-Shot Learning: Agricultural Domain Use-Case
Large datasets' availability is catalyzing a rapid expansion of deep learning in general and computer vision in particular.
Learning To Count Everything
cvlab-stonybrook/LearningToCountEverything
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CVPR 2021
We also present a novel adaptation strategy to adapt our network to any novel visual category at test time, using only a few exemplar objects from the novel category.
Class-agnostic-Few-shot-Object-Counting
SinicaGroup/Class-agnostic-Few-shot-Object-Counting
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WACV 2021
Instead of counting a pre-defined class, our model is able to count instances based on input reference images and reduces the huge cost of data collection, training and parameter tuning for each new object class.
Spatial-Temporal Super-Resolution of Satellite Imagery via Conditional Pixel Synthesis
KellyYutongHe/satellite-pixel-synthesis-pytorch
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NeurIPS 2021
High-resolution satellite imagery has proven useful for a broad range of tasks, including measurement of global human population, local economic livelihoods, and biodiversity, among many others.
An Accurate Car Counting in Aerial Images Based on Convolutional Neural Networks
ekilic/Heatmap-Learner-CNN-for-Object-Counting
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Journal of Ambient Intelligence and Humanized Computing 2021
The proposed model, called heatmap learner convolutional neural network (HLCNN), is used to predict the heatmap of target car instances.
Automating cell counting in fluorescent microscopy through deep learning with c-ResUnet
Counting cells in fluorescent microscopy is a tedious, time-consuming task that researchers have to accomplish to assess the effects of different experimental conditions on biological structures of interest.
