Gadde Sai Shailesh

Data Science Skills

Artificial Intelligence	Statistical Modeling
Visualization	Tableau

Domain Skills

Health Care

Crowd Counting

Category: Domain Usecases

Sub-Category: Others

Use-case Type: Object Detection

Tags: artificial_intelligence,computer_vision,deep_learning,object_detection

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Note: Certain Model Inferences can take long time for the first run (Warmup) and would get faster with the subsequent inferences. We thank you for your patience.

Use Case Summary

Problem Statement

This model was built in order to explore an application of Computer Vision known as Crowd counting.Crowd Countingis a task to count people in an image. It is mainly used in real life for automated public monitoring such as surveillance and traffic control. Different from object detection, Crowd Counting aims at recognizing arbitrarily sized targets in various situations including sparse and cluttering scenes at the same time.

Dataset Description

We used theShanghaiTech datasetit is a introduce a new large-scale crowd counting dataset named Shanghaitech which contains 1198 annotated images, with a total of 330,165 people with centres of their heads annotated.

As far as we know, this dataset is the largest one in terms of the number of annotated people. This dataset consists of two parts: there are 482 images in Part A which are randomly crawled from the Internet, and 716 images in Part B which are taken from the busy streets of metropolitan areas in Shanghai. The crowd density varies significantly between the two subsets, making accurate estimation of the crowd more challenging than most existing datasets. Both Part A and Part B are divided into training and testing: 300 images of Part A are used for training and the remaining 182 images for testing, and 400 images of Part B are for training and 316 for testing

How does the model work?

We first create a density map for the objects. Then, the algorithm learns a linear mapping between the extracted features and their object density maps.We can also use random forest regression to learn non-linear mapping. Our model will first predict the density map for a given image. The pixel value will be 0 if no person is present. A certain pre-defined value will be assigned if that pixel corresponds to a person. So, calculating the total pixel values corresponding to a person will give us the count of people in that image.

How to use the model?

1) Choose the crowd image that you want to predict(Aerial view of the image is preferred).
2) Click predict.
3) Voila! You can see your output now!.

What are the metrics used to evaluate the model?

The evaluation metric used in CSRNet is MAE and MSE, i.e., Mean Absolute Error and Mean Square Error. These are given by:

Results of the model