Description:

Many residential society administrations, tolls, business complexes and parking spaces in India lack an automated system for car parking and vehicle monitoring. Most of the commercial and residential places face an impending problem of illegal car/vehicle parking inside their premises. This issue is not bounded to just parking, but also adds to the security concerns inside those establishments. Though there are solutions that exist in the market, the affordability comes into question. In this regard, we would like to have an affordable solution that caters to the Indian markets

Expectation:

For the team choosing this topic, we expect to have a working model / infrastructure of the solution wherein, when a vehicle passes through a check post and the solution identifies as many features as it can regarding the situation of the vehicle. All Teams will be provided with an amount of video footage. The teams must build a fictitious database based on the vehicles appearing in footage and demonstrate the problems that they are able to solve/automate in existing infrastructure. (For example, if a team is trying to solve problems of a residential complex, they might create a database of some of the vehicles appearing in the footage and list them as residents and others not registered in the database to be considered as visitors). Each team is supposed to download the data and annotate it according to the features they are going to build. Teams must come up with new features for monetization of their business model and must build machine learning models (for preliminary shortlisting and finals) and required mobile/web applications(for finals only) reflecting the same features . Teams are requested to share the annotations done by them on the video data and the pattern of annotation. Teams are requested to build the machine learning model and share a working code on github (privately with @chetanbansiwal or publically)

Teams will be judged on the following grounds in the same order as written for the next round

1. Accuracy of the model in performing the expected task.
2. Novelty of the Business model.
3. Speed of the model in performing the expected task.
4. The precision followed during annotation of data.
5. The relevance of annotations considering the problem the team is expecting to solve with it.

Dataset :

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