About
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
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Datasets
Greatest papers with code
You Only Look Once: Unified, Real-Time Object Detection
CVPR 2016
• thtrieu/darkflow
• 
A single neural network predicts bounding boxes and class probabilities directly from full images in one evaluation.
Ranked #1 on
Real-Time Object Detection
on PASCAL VOC 2007
CNN-based Density Estimation and Crowd Counting: A Survey
• gjy3035/Awesome-Crowd-Counting
• 
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.
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.
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.
Ranked #2 on
Object Counting
on COCO count-test
Towards Partial Supervision for Generic Object Counting in Natural Scenes
Our RLC framework further reduces the annotation cost arising from large numbers of object categories in a dataset by only using lower-count supervision for a subset of categories and class-labels for the remaining ones.
IMAGE CLASSIFICATION INSTANCE SEGMENTATION OBJECT COUNTING SEMANTIC SEGMENTATION
Object Counting and Instance Segmentation with Image-level Supervision
CVPR 2019
• GuoleiSun/CountSeg
•
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.
Ranked #1 on
Object Counting
on COCO count-test
From Open Set to Closed Set: Supervised Spatial Divide-and-Conquer for Object Counting
• xhp-hust-2018-2011/S-DCNet
•
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.
Drone Based RGBT Vehicle Detection and Counting: A Challenge
In this paper we present a large-scale vehicle detection and counting benchmark, named DroneVehicle, aiming at advancing visual analysis tasks on the drone platform.
Class-Agnostic Counting
• erikalu/class-agnostic-counting
•
The model achieves competitive performance on cell and crowd counting datasets, and surpasses the state-of-the-art on the car dataset using only three training images.
Synbols: Probing Learning Algorithms with Synthetic Datasets
Progress in the field of machine learning has been fueled by the introduction of benchmark datasets pushing the limits of existing algorithms.
ACTIVE LEARNING FEW-SHOT LEARNING OBJECT COUNTING UNSUPERVISED REPRESENTATION LEARNING