To solve the above issues, we collect a large-scale object localization and counting dataset at 28 different stores and apartments, which consists of 50,394 images with the JPEG image resolution of 1920x1080 pixels. More than 1.9 million object instances in 140 categories (including *Jacket*, *Shoes*, *Oven*, etc.) are annotated.
To facilitate data usage, we divide the dataset into two subsets, i.e., *training* and *testing* sets, including 34,022 images for training and 16,372 images for testing.
As mentioned above, we acquire the dataset at $28$ different stores and apartments. The dataset contains 140 common commodities, including 9 big subclasses, i.e., Baby Stuffs (e.g., *Baby Diapers* and *Baby Slippers*), Drinks (e.g., *Juice* and *Ginger Tea*), Food Stuff (e.g., *Dried Fish* and *Cake*), Daily Chemicals (e.g., *Soap* and *Shampoo*), Clothing (e.g., *Jacket* and *Adult hats*),
Electrical Appliances (e.g., *Microwave Oven* and *Socket*), Storage Appliances (e.g., *Trash* and *Stool*), Kitchen Utensils (e.g., *Forks* and *Food Box*), and Stationery and Sporting Goods (e.g., *Skate* and *Notebook*).
There are various factors challenging the performance of algorithms, such as scale changes, illumination variations, occlusion, similar appearance, clutter background, blurring and deformation, *etc*.
## Evaluation protocol
@@ -27,12 +32,6 @@ which takes both the localization and counting accuracies into account. For more
## Baseline method
## Abstract
The convention standard for object detection uses a bounding box to represent each individual object instance. However, it is not practical in the industry-relevant applications in the context of warehouses due to severe occlusions among groups of instances of the same categories. In this paper, we propose a new task, ie, simultaneously object localization and counting, abbreviated as *Locount*, which requires algorithms to localize groups of objects of interest with the number of instances. However, there does not exist a dataset or benchmark designed for such a task. To this end, we collect a large-scale object localization and counting dataset with rich annotations in retail stores, which consists of 50,394 images with more than 1.9 million object instances in 140 categories. Together with this dataset, we provide a new evaluation protocol and divide the training and testing subsets to fairly evaluate the performance of algorithms for Locount, developing a new benchmark for the Locount task. Moreover, we present a cascaded localization and counting network as a strong baseline, which gradually classifies and regresses the bounding boxes of objects with the predicted numbers of instances enclosed in the bounding boxes, trained in an end-to-end manner. Extensive experiments are conducted on the proposed dataset to demonstrate its significance and the analysis discussions on failure cases are provided to indicate future directions.
## Citation
If you find this dataset useful for your research, please cite
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@@ -52,8 +51,5 @@ The *training* subset includes 34,022 images.
The *testing* subset includes 16,372 images.
## Evaluation
To fairly compare algorithms on the *Locount* task, we design a new evaluation protocol, which penalizes algorithms for missing object instances, for duplicate detections of one instance, for false positive detections, and for false counting numbers of detections. Please check our paper for details.
## Feedback
Suggestions and opinions of this dataset are welcome. Please contact the authors by sending email to libo@iscas.ac.cn.
