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.

OBJECT COUNTING REAL-TIME OBJECT DETECTION

CNN-based Density Estimation and Crowd Counting: A Survey

28 Mar 2020gjy3035/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.

CROWD COUNTING DENSITY ESTIMATION OBJECT COUNTING

Towards perspective-free object counting with deep learning

journal 2016 gramuah/ccnn

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.

OBJECT COUNTING

Where are the Blobs: Counting by Localization with Point Supervision

ECCV 2018 ElementAI/LCFCN

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.

OBJECT COUNTING

Towards Partial Supervision for Generic Object Counting in Natural Scenes

13 Dec 2019GuoleiSun/CountSeg

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.

INSTANCE SEGMENTATION OBJECT COUNTING SEMANTIC SEGMENTATION

From Open Set to Closed Set: Supervised Spatial Divide-and-Conquer for Object Counting

7 Jan 2020xhp-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.

OBJECT COUNTING

Drone Based RGBT Vehicle Detection and Counting: A Challenge

5 Mar 2020VisDrone/DroneVehicle

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.

OBJECT COUNTING OBJECT DETECTION

Class-Agnostic Counting

1 Nov 2018erikalu/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.

CROWD COUNTING FEW-SHOT LEARNING OBJECT COUNTING

Synbols: Probing Learning Algorithms with Synthetic Datasets

NeurIPS 2020 ElementAI/synbols

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