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
Latest papers with no code
ChatGPT and general-purpose AI count fruits in pictures surprisingly well
We interpret these results as two surprises for deep learning users in applied domains: a foundation model with few-shot domain-specific learning can drastically save time and effort compared to the conventional approach, and ChatGPT can reveal a relatively good performance.
Counting Objects in a Robotic Hand
A robot performing multi-object grasping needs to sense the number of objects in the hand after grasping.
OmniCount: Multi-label Object Counting with Semantic-Geometric Priors
Object counting is pivotal for understanding the composition of scenes.
Effectiveness Assessment of Recent Large Vision-Language Models
The advent of large vision-language models (LVLMs) represents a noteworthy advancement towards the pursuit of artificial general intelligence.
AFreeCA: Annotation-Free Counting for All
Consequently, we can generate counting data for any type of object and count them in an unsupervised manner.
A Density-Guided Temporal Attention Transformer for Indiscernible Object Counting in Underwater Video
Dense object counting or crowd counting has come a long way thanks to the recent development in the vision community.
Enhancing Zero-shot Counting via Language-guided Exemplar Learning
Recently, Class-Agnostic Counting (CAC) problem has garnered increasing attention owing to its intriguing generality and superior efficiency compared to Category-Specific Counting (CSC).
Do Object Detection Localization Errors Affect Human Performance and Trust?
Bounding boxes are often used to communicate automatic object detection results to humans, aiding humans in a multitude of tasks.
Diffusion-based Data Augmentation for Object Counting Problems
Our proposed smoothed density map input for ControlNet significantly improves ControlNet's performance in generating crowds in the correct locations.
Real-Time Object Detection in Occluded Environment with Background Cluttering Effects Using Deep Learning
The accuracy and frame per second of the SSD-Mobilenet v2 model are higher than YOLO V3 and YOLO V4.
