Object Counting
60 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
YOLO9000: Better, Faster, Stronger
AlexeyAB/darknet
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CVPR 2017
On the 156 classes not in COCO, YOLO9000 gets 16. 0 mAP.
You Only Look Once: Unified, Real-Time Object Detection
AlexeyAB/darknet
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CVPR 2016
A single neural network predicts bounding boxes and class probabilities directly from full images in one evaluation.
Synbols: Probing Learning Algorithms with Synthetic Datasets
ElementAI/baal
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NeurIPS 2020
Progress in the field of machine learning has been fueled by the introduction of benchmark datasets pushing the limits of existing algorithms.
Where are the Blobs: Counting by Localization with Point Supervision
However, we propose a detection-based method that does not need to estimate the size and shape of the objects and that outperforms regression-based methods.
From Open Set to Closed Set: Supervised Spatial Divide-and-Conquer for Object Counting
xhp-hust-2018-2011/S-DCNet
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Visual counting, a task that aims to estimate the number of objects from an image/video, is an open-set problem by nature, i. e., the number of population can vary in [0, inf) in theory.
CNN-based Density Estimation and Crowd Counting: A Survey
gjy3035/Awesome-Crowd-Counting
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Through our analysis, we expect to make reasonable inference and prediction for the future development of crowd counting, and meanwhile, it can also provide feasible solutions for the problem of object counting in other fields.
Real Time Pear Fruit Detection and Counting Using YOLOv4 Models and Deep SORT
AlexeyAB/darknet
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Sensors 2021
In terms of accuracy, YOLOv4-CSP was observed as the optimal model, with an AP@0. 50 of 98%.
Self-Supervised Learning from Images with a Joint-Embedding Predictive Architecture
facebookresearch/ijepa
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CVPR 2023
This paper demonstrates an approach for learning highly semantic image representations without relying on hand-crafted data-augmentations.
Towards perspective-free object counting with deep learning
Essentially, the CCNN is formulated as a regression model where the network learns how to map the appearance of the image patches to their corresponding object density maps.
Improving Object Counting with Heatmap Regulation
littleaich/heatmap-regulation
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Adding HR to a simple VGG front-end improves performance on all these benchmarks compared to a simple one-look baseline model and results in state-of-the-art performance for car counting.
