Intelligent Traffic Analysis for Enhanced Operational Efficiency.

Through collaboration, innovation, and a data-driven approach, Sinenza empowered our client to transform their traffic analysis processes, yielding substantial operational and cost efficiencies.
The Customer
Our client, a leading provider of traffic, transport, and mobility data solutions, specializes in supporting strategic planning, traffic impact assessments, property management, and retail evaluations. Their focus extends to optimizing existing transport management systems to enhance overall efficiency.
The Challenge
Our engagement with the client centered around addressing a critical issue - accurate traffic counting. The primary objective was to identify and tally passing vehicles on non-real-time street camera surveillance videos, categorizing them by type (car, truck, bus, etc.). Subsequently, the project expanded to include license plate recognition.
Initial scenario
Prior to our intervention, the client relied on a manual approach to vehicle counting. They had developed a rudimentary software tool that utilized video input and a game controller for efficient counting and categorization of vehicles. Although this tool expedited the counting process, human involvement remained necessary. Despite efforts to streamline the process and outsource counting tasks to low-cost workers, such as those in India, the profitability of this approach was limited. Demand grew as new customers were acquired, but challenges persisted.
Decision to innovate
Recognizing the potential for automation, the client explored the development of advanced counting software. Cloud computing costs were projected to be significantly lower than human labor expenses. Furthermore, automation could deliver unparalleled accuracy, overcoming the shortcomings of manual counting. The pivotal concern centered on the investment required for development and the anticipated returns over time.
Why Sinenza?
Our team's expertise in image recognition and neural network development positioned us as the ideal partner for this project. We provided transparent insights into the functionality of AI algorithms, detailing client involvement such as data collection and tool training. We proposed a comprehensive workflow encompassing vehicle identification, tracking, and fine-tuning of output. Our well-defined accuracy metrics offered confidence and enabled easy comparison with human performance. We presented an adaptable, fully automated processing architecture on the Azure cloud platform.
Our Solution
Our initial focus was extensive research to identify cutting-edge solutions for object detection and tracking. After customizing these solutions to our needs, we embarked on training data generation. We developed an intuitive data labeler tool and collaborated with the client to outsource labeling to their manual counting workforce. While the training dataset's quantity was satisfactory, the quality suffered due to the workforce's prior focus on simple counting rather than precise labeling.
Synthetic Data Solution
Recognizing the limitations, we adopted a synthetic data approach. Leveraging a manipulable computer game, we devised a processing flow to extract training data from within the render pipeline. This approach yielded a substantial volume of high-quality data, vastly surpassing our performance goals.
Refined User Interface
Simultaneously, we developed a user-friendly interface empowering the client to fine-tune the processing flow for specific scenarios, such as night or inclement weather conditions.
Cloud Deployment
and Expansion
Each stage of our pipeline found its place in the cloud, enabling an end-to-end solution to automatically process camera surveillance videos. Scalability and cost-effectiveness were key priorities. In a subsequent phase, we extended the solution to include license plate recognition under optimal visibility conditions and high-resolution videos. Our previously established data-gathering mechanism proved instrumental in meeting these new requirements.
With our solution in place, the client achieved successful processing for 70% of their surveillance videos without requiring manual intervention. An additional 85% of videos necessitated only minimal refinement. This translated to a significant 70% reduction in recurring processing costs, achieved by minimizing reliance on manual labor. Remarkably, the development costs were fully recouped within a year of implementation.
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