Concluding Notes: The Crowd Density Project

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Secure and Private AI Writing Challenge

5 min readAug 19, 2019

Summarizing our amazing experience on The Crowd Density Project at SPAIC 2019

It was somewhere around the start of my Secure and Private AI Scholarship Challenge Journey, that I happened to discover the amazing study group #sg_wonder_vision. Working with the fantastic individuals on this study group has been one of the most valuable experiences I have had on SPAIC 2019.

Project Background

The main objective of this project was to drive towards building a self-sufficient software, that can accurately predict whether or not a stampede is imminent, based on the incoming live visuals of a place. Often, huge footfall is associated with various popular festivals, religious ceremonies, public events, concerts etc. The enormous count of people coupled with their density(how close they are to each other) easily poses the risk of a normal crowd turning into a dangerous stampede that could potentially cost many lives. This has happened several times in the past, especially at the world-famous Kumbh Mela(a festival where 30 million people take part in Allahabad, when the city can only hold about 9 million), and the recent tragedy at Elphinstone Railway station, Mumbai. Most current stampede prevention solutions are reliant on hardware(footfall mats based on sensors etc), bringing along with it the question of capital investment, installation and maintenance costs and issues in the long run. We hope to come up with a completely software-based solution for this problem, that can provide good levels of accuracy to accurately predict stampedes, so that some kind of intervention can be done at the right time to prevent such incidents in the future.

Our Purpose

More than 3000 people have lost their lives in stampedes between 2000 and 2015, in India alone. The tragic stampedes during the Hajj pilgrimage at Mina in 2015 at the Elphinstone Railway Station in 2017 stand forever as reminders that stampedes are always looming threats on huge crowds. This statistic is enough and more proof that stampedes are among the most underestimated, and overlooked disasters worldwide.

We believe that human stampedes are very much preventable disasters. It is absolutely unacceptable that even in this advanced age where technology has the power to prevent such incidents, so many lives are being lost in this manner. Every human being at every public gathering, deserves to return home safely.

Our Vision for this project

To give Crowd Density Detection the platform of significance that it deserves, with regards to safety at every public gathering.
To explore current trends in Crowd Density Detection.
To make consistent efforts towards building pure software-based solutions that can give high accurate information that can help detect and prevent imminent stampedes.

Exploration

Throughout the course of our journey on the Facebook Secure and Private AI Challenge 2019, we have tried to explore this topic from every angle. Right from the various use cases of Crowd Density Detection to the varieties of different models and data sets used, we have tried to make our exploration as comprehensive as possible within our short journey as a team. Here, you can see a collation of all of these efforts.

The Future : Improving the Project with Secure and Private AI

We see a lot of scope to further work on this project. We think that Secure and Private AI can be significantly employed on this project to achieve the following:

Encrypt the identity of individuals in crowds, and ensure that only the requisite statistical analysis is obtained from the Crowd Density Detector(no particulars about any individual is retrievable)(Scope for incorporating Differential Privacy)
Federated Learning and Secure Aggregation can be employed during large-scale events, that involve a network of cameras recording live visuals of various areas of the venue or sub-events.
In this case, the model would be downloaded to each station(surveillance unit for a particular area), and it would train on the live visuals being generated at that area.
The results from multiple such stations would be uploaded and averaged before being used to update the model at the central server, which gains better crowd counting/predicting ability as a result of this iterative process.
This smarter model is then downloaded and employed at all the stations supervising the different areas.

Reinforcing the Aim of this Project

Stampede Detection and Prevention is among the most overlooked and underestimated disasters, for which there is usually very less preparedness. But this is an attitude that must change. Stampedes are such dynamic situations, so much so that a crowd that seemed normal can turn into a violent stampede in a matter of seconds.
The only way to prevent such incidents is to ensure that crowds that exceed the capacity of a venue are never formed; this can be done only by actively monitoring crowds and keeping a tab on COUNTING the number of individuals in the crowd. If the count exceeds the safe threshold, the authorities must take precautionary measures to prevent am imminent stampede at all costs. This is exactly where The Crowd Density Project comes in.
‘PREVENTION IS BETTER THAN CURE’ — This is the saying that this project embodies. Managing a crowd that is ALREADY in a stampede is not an option(this is near impossible). So, the best and only option at preventing such incidents and protecting human lives in such crowds, is to maintain active surveillance, send alerts in the event of threshold-exceeding counts and take crowd redirection measures accordingly.
We sincerely hope that this project and the collation on this website inspires more tech enthusiasts to see the real challenge that crowds and stampedes are. Human lives are too precious to be lost on avoidable incidents like these. This website collation was intended to serve as a starting ground for anyone who is trying to explore the field of crowd counting and its applications. We understand that getting one’s feet wet in a new technical area can be challenging, and all our blogs and implementations are aimed at helping anyone who is trying to explore this field for the first time.

A ‘Note’ of Thanks

On behalf of the entire team comprising myself(Pooja Vinod), Sreekanth Zipsy, Ramkrishna Acharya and Suraiya Khan, I would like to thank the Facebook Udacity Secure and Private AI Scholarship Challenge Community 2019 for being the most supportive space for this project. We would like to thank the entire team at #sg_wonder_vision for encouraging us throughout and providing all the help we needed.
I would also like to thank the amazing technologists out there, who truly make the world of Computer Vision easy to explore. My sincere thanks to Adrian Rosebrock of PyImageSearch, whose blogs and tutorials were my guideposts to many successful implementations. Thanks also go out to the AnalyticsVidhya blog and the authors of the C3 framework for helping us explore the world of crowd counting further.
Last but not the least, I want to thank my awesome teammates without whose support and cooperation, this project would have never reached realization.

See our GitHub repo: https://github.com/poojavinod100/People-Counting-Crowd-Density-Detection

See the exclusive website we created for our project: https://poojavinod100.wixsite.com/crowddensityproject