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Crowd Management


What it is ?
Crowd Management deals with - How to come up with strategies to  better manage large gatherings

Why is it needed?

Many things can go wrong in large crowds 
  1. Outbreak
  2. Congestion
  3. Stampede
  4. Fights
  5. Disorder
  6. Missing People
How is it relevant now?

Big Data is being collected using UAVs, CCTVs etc.,
 Can we harness it to help solve some issues ?
Ex: Work by Prof. K.S. Rajan during Godavari Pushkaralu

Our Work

We took up one specific problem of Crowd Management which is Identification of Missing People. 
This is a very common issue that we observe in large gatherings. Here are some numbers that will justify this. 120,000,000 pilgrims attended the last Prayagraj Kumbh Mela in 2013, equivalent to the population of Japan. According to an official in charge of running the lost-found system, around 90% of the people who get lost are illiterate. He also adds that most people will spend only a few hours being lost. There's a 70% chance they get reunited with their families on the same day, 90% chance they get reunited within a few weeks and 99% chance they get reunited with their families by the end of the Kumbh Mela. Others go missing for dark reasons. Large events increase the risk of trafficking and in a place as crowded as the Mela, children go missing very easily. Traffickers see large events like these an opportunity. 

 How we did it ?

 2 step process

1. Identification
Given a photo of missing person, we ranked all the visible faces in the crowd based on a similarity score. 
The method first detects the faces and then using the features extracted it predicts the various landmarks on the face, which it uses to identifies the face.


 In the figure, we trained the model with the photo on the right. We then ranked each face in the photo on the right based on how close it is to the trained image. We can observe that it gave rank 1 to the person in the trained photo.
2. Tracking
Once we identify the face, we can select the face in the footage as a Region Of Interest . Then the algorithm tracks the path of  missing person.


Feature selection is done using discriminative correlation filter which supports channel and spatial reliability

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