March 24, 2023

Zedroll

Intro to Zedroll

In today’s bustling shopping malls, ensuring the safety and security of visitors and assets is a multifaceted challenge. Traditional security methods, relying heavily on human surveillance and manual monitoring, often struggle to keep pace with the dynamic nature of these environments. To address these challenges, our project introduces a groundbreaking approach that harnesses the power of computer vision, video transcription, and IoT integration to enhance security and operational efficiency in large retail spaces.

Central to the system is the utilization of advanced computer vision technology, enabling security cameras not only to capture footage but also to analyze it in real time. By leveraging sophisticated algorithms, the system can detect suspicious activities such as unauthorized access, loitering, or unusual behavior. Immediate alerts can then be sent to security personnel, allowing for timely intervention. Moreover, the system’s ability to track individuals throughout the mall provides valuable data for crowd management and helps optimize staffing levels to ensure a safe and pleasant shopping experience for visitors.

In addition to computer vision, the system incorporates video transcription capabilities. This feature makes it easier for security personnel to search through hours of footage to find specific incidents or conversations, saving valuable time in investigations and enhancing the overall effectiveness of security operations.

An innovative aspect of the system is its integration with IoT devices. By connecting security cameras with IoT sensors strategically placed throughout the mall, the system can gather additional data such as foot traffic patterns, temperature, and humidity levels. This data can then be used to optimize the layout of the mall, improve energy efficiency, and enhance the overall shopping experience for visitors. Additionally, by analyzing this data, the system can provide valuable insights into mall operations, helping management make informed decisions to further enhance security and operational efficiency.

In conclusion, this system represents a significant advancement in mall security, combining state-of-the-art technology with innovative IoT integration. By enhancing the effectiveness of surveillance systems and providing valuable insights into mall operations, the system is poised to revolutionize the way large shopping complexes approach security and management. With its ability to enhance security measures, optimize operations, and improve the overall visitor experience, this system is set to become a cornerstone in the evolution of mall security and management practices.

Details:

Project Overview

Zedroll is a pioneering initiative aimed at tackling theft in chain stores, with a bold vision of reducing

incidents by up to 90% while significantly enhancing cost savings. The project introduces a sophisticated

system that furnishes daily reports to store managers, not only detecting theft but also proactively

preventing it.

Market Cap: With its ambitious goals and innovative approach, Zedroll’s potential market reach is

substantial, tapping into the pressing need for advanced security solutions within the retail industry.

Founder and Tech owner: UNAGI, No VC firm yet.

Completion of ML crime detection and video content, transcription models, and cloud pipelines: 30%

Features of the Project

*BIGDATA handling challenge

The project boasts a range of cutting-edge features designed to revolutionize theft prevention:

Anomaly Detection: Harnessing the power of machine learning algorithms, Zedroll scrutinizes real-time

camera feeds to swiftly identify deviations or anomalies in in-store activities.

Behavioral Analysis: Through advanced ML models, the system delves deep into individual behavior patterns captured in camera footage, enabling the pinpointing of suspicious activities. connecting with security individuals of the system on application.

Pattern Recognition: Over time, the project’s ML algorithms become adept at recognizing recurring patterns of suspicious behavior, enabling the early detection of potential theft incidents.

Continuous Improvement: Zedroll’s dynamic ML algorithms are perpetually evolving, continuously learning from fresh data to ensure adaptability and efficacy in identifying theft patterns. Additionally, the system remains poised to incorporate cutting-edge face recognition technology for heightened security measure.