Software To Help You Turn Your Data Into AI
Forget fragmented workflows, annotation tools, and Notebooks for building AI applications. Encord Data Engine accelerates every step of taking your model into production.
Automotus is a leading technology startup, building curbside management automation solutions. Their team has rapidly grown their reach and customer base, raising a $9M seed round in 2022, and now working with cities, airports, and fleets all over the world to improve urban mobility – reducing congestion, emissions, and traffic hazards, optimizing curbside utilization, and monetizing various loading activities.
Their software enables customers to better understand the curb, simplify payments, and enforce relevant regulation. To do so, the Automotus team has built cutting-edge computer vision technology to capture real-time data from strategically-mounted street cameras.
We sat down with Prajwal Kotamraju, Co-Founder and Head of Computer Vision at Automotus, to discuss their journey to where they are today, his work overseeing the product roadmap, and the exciting plans ahead for the business.
One of the team’s first priorities was managing the infrastructure constraints, as requirements and availability varied heavily depending on the location that the cameras were placed in. After some trial and error, the team had a network of cameras up and running, and started working on converting the vast amount of de-identified images into labeled training data.
The model needed to identify, locate, and classify vehicles, analyze movement to understand traffic flows, and much more. Managing different fields of view, conditions, and labeling approaches was paramount.
After evaluating a few other tools, the Automotus team decided to partner with Encord.
On the topic of labeled data, Prajwal added: “For example, a shortcoming with other tools was the quality of the labels: we’d occasionally realize bounding boxes would be tighter or too wide around the objects they were identifying, or objects wouldn’t be classified correctly within frames. Now, we can select the sampling rate of frames that we want to move towards a review process, and share real-time context with annotators so that they can also power our model performance. This human-in-the-loop approach means we can use Encord to help our annotators perform annotations better, which in turn speeds up how quickly we can improve our model performance. We are able to localize objects better and increase accuracy.”
But most importantly, the high model accuracy enabled Automotus to better serve and grow with their customers – presenting more accurate data to their clients: “From the modality distribution that happens at the street-level, to more accurate representations of the dwell times [and a few other metrics that we supply to our clients] – these base models are the ones driving these analytics.”
Having set up a continuous, iterative annotation pipeline, the Automotus team turned their attention to the next question: Out of all the data collected, how could they ensure they were labeling the right data? And how would they know what data that is?
Capturing large collections of de-identified images from hundreds of cameras means there are large troves of data available, but labeling all the data doesn’t necessarily lead to improvements in the model performance, and is expensive, so they sought to identify which data actually drove the most results.
Using Encord Active, the team was able to visually inspect, query, and sort their datasets to remove unwanted and poor quality data with just a few clicks, leading to a 35% reduction in the size of the datasets for annotation. In turn, this enabled the team to cut their labeling costs by over a third.
“We now have an integrated, one-stop solution where we can manage our data and also understand our model performance to create feedback mechanisms to improve data and models.”
With the improved models, the team were able to successfully extend pilot programs, expanding to more locations and improving the quality of the data they can present to clients.”
Over the last 3 years, the Automotus team has built an industry-leading product that serves hundreds of municipalities, fleets, and airports all over the world. The team has been able to rapidly grow with their customers – increasing their availability geographically, and simplifying smart loading zones further – and it’s been incredible to see their journey to this day.
We’re so proud to work with Prajwal and the Automotus team and we look forward to all that’s ahead!
Software To Help You Turn Your Data Into AI
Forget fragmented workflows, annotation tools, and Notebooks for building AI applications. Encord Data Engine accelerates every step of taking your model into production.