Object Counting
43 papers with code • 8 benchmarks • 20 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
COCO
PASCAL VOC
PASCAL VOC 2007
FSOD
CARPK
COWC
FSC147
TRANCOS
Most implemented papers
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.
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.
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.
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%.
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.
Object Counting and Instance Segmentation with Image-level Supervision
GuoleiSun/CountSeg
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CVPR 2019
Moreover, our approach improves state-of-the-art image-level supervised instance segmentation with a relative gain of 17. 8% in terms of average best overlap, on the PASCAL VOC 2012 dataset.
Drone-based RGB-Infrared Cross-Modality Vehicle Detection via Uncertainty-Aware Learning
To address this dilemma, we further propose an uncertainty-aware cross-modality vehicle detection (UA-CMDet) framework to extract complementary information from cross-modal images, which can significantly improve the detection performance in low light conditions.
Encoder-Decoder Based Convolutional Neural Networks with Multi-Scale-Aware Modules for Crowd Counting
Pongpisit-Thanasutives/Variations-of-SFANet-for-Crowd-Counting
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Inspired by SFANet, the first model, which is named M-SFANet, is attached with atrous spatial pyramid pooling (ASPP) and context-aware module (CAN).
