Surveillance video processing

Introduction

In a world where video data continues to grow exponentially, the ability to extract actionable insights from surveillance footage provides a crucial edge for many businesses. However, processing such large volumes of video to glean valuable information remains a time-intensive task, often reliant on manual intervention. Recognizing the need for automation, SmartCat partnered with a data-driven spatial analytics company to create an advanced system that transforms surveillance videos into usable data, enabling seamless insight generation without the need for human oversight.

Challenge

The key challenge was the need to accurately identify and track objects of interest within surveillance videos, translating raw video data into an organized, actionable format. The client’s goals included:

• Transforming video into structured data for insight generation.
• Creating a flexible solution that could adapt to various camera specifications.
• Achieving a high detection accuracy rate for objects and individuals.

Manual analysis was too slow and labor-intensive to meet these needs, so an automated system capable of interpreting video data was essential.

Solution

SmartCat approached the problem with a multi-phase development process:

1. Proof of Concept (PoC): We began with a PoC to test the feasibility of detecting and tracking objects of interest with high accuracy. Multiple algorithms were developed and tested, establishing a foundation for the larger solution.
2. Defining System Components: We identified system elements that could be standardized and those that required customization to work with specific camera configurations. This distinction allowed us to optimize processing and integration times for different client scenarios.
3. Pilot Project: Moving beyond the PoC, we initiated a pilot project focused on training camera-specific networks to recognize and track objects accurately. This included developing a structured data output format and post-processing methods to ensure compatibility with client needs.

Automated Secondary Analysis: The final stage involved analyzing raw data from surveillance footage. Using a large private database, we trained models to extract various types of information, which was then refined through secondary analysis, creating insights, alerting mechanisms, and visualizations for user interpretation.

Smart Tip

Automating video data analysis not only saves time but can provide real-time insights that drive business decisions. Customizing parts of the model to camera-specific requirements enhances accuracy and flexibility.

Smart Fact

Automated object detection and tracking can increase data processing speeds by up to 70%, providing valuable insights far faster than traditional manual methods.

Results

Our solution successfully automated the extraction of insights from video data, yielding key benefits for the client:

Customizable and Scalable System: The system’s modular design allows it to be adapted easily to different camera types, enabling rapid scaling and integration.

High Detection Accuracy: Achieved up to 90% accuracy in identifying people and objects, ensuring that the insights generated were reliable and actionable.

Automated Data Transformation: Video data was automatically manipulated to meet client requirements, reducing the need for manual processing and significantly speeding up insight generation.

Technologies used

• Computer Vision Algorithms: Object detection and tracking models.
• Machine Learning Models: Custom-trained networks for camera-specific detection.
• Post-Processing Frameworks: For refining and transforming raw data into structured insights.

About the Client

Our client is a leader in spatial analytics with a focus on understanding patterns in human behavior. Leveraging a vast collection of surveillance footage, they sought a solution that would allow for the automated extraction of insights from video data to improve their predictive and behavioral analytics capabilities. By automating data extraction, they aimed to better support their customers in forecasting user needs and accommodating them more effectively.