Enhancing Safety Video Analytics through High-Quality Data Annotation


Safety video analytics has revolutionized theway companies approach security and safety in various industries. By using advanced artificial intelligence (AI) algorithms, these systems can quickly detect potential threats or safety violations, enabling rapid response and reducing risks. In this case study, we explore how Tictag played a crucial role in enhancing the performance of a safety video analytics system.


SafetyTech, a reputable safety videoanalytics company, operates in various industries to provide advanced security and safety solutions. With a growing number of safety concerns and potential threats, SafetyTech aimed to enhance the accuracy and efficiency of theirAI-powered safety video analytics system. Their objective was to deliver areliable and effective system that enables rapid response and risk reduction.


SafetyTech aimed to enhance the accuracy and efficiency of their AI-powered safety video analytics system. The primary objectives were:

  1. Improve threat and safety violation detection rates.
  2. Reduce false alarms and improve system reliability.
  3. Speed up the decision-making process for safety and security professionals.


Inaccurate AI algorithms: SafetyTech's existing algorithms were not performing at the desired level of accuracy, impacting their ability to detect threats and safety violations effectively.

Insufficient labeled training data: SafetyTech lacked a sufficient amount of high-quality labeled training data required to train their AI system and improve its performance.

Time-consuming data annotation: Annotating safety andsecurity video footage with precision and accuracy can be a labor-intensive and time-consuming task, requiring significant resources.

Maintaining annotation quality: Ensuring a high level of annotation accuracy throughout the process posed a challenge, as errors or inconsistencies could impact the reliability of the AI system.

Balancing dataset diversity: Curating abalanced and diverse dataset that covers various safety scenarios and environments was crucial to ensure the AI system's performance across different situations, posing a challenge in dataset curation.


Tictag provided a comprehensive data annotation solution to address SafetyTech's needs. The process involved the following steps:

  1. Data Collection: Tictag gathered a vast repository of safety and security video footage from various sources, including SafetyTech's clients, open-source datasets, and other relevant industries.
  2. Data Annotation: Tictag's skilled annotators labeled the video data with precision and accuracy, identifying potential safety hazards, such as fire, unattended objects, or unauthorized access. The annotation process included the use of bounding boxes, polygons, and semantic segmentation to ensure maximum detail.
  3. Quality Assurance: Tictag implemented a rigorous quality assurance process, including multiple review stages, to maintain a high level of annotation accuracy.
  4. Dataset Curation: Tictag curated the annotated video dataset to provide a balanced and diverse set of examples for the AI model. This process ensured that the AI system would perform well across various scenarios and environments.
  5. Integration &Training: SafetyTech integrated the annotated data into their AI algorithms, retraining their models to achieve improved accuracy and reliability.


After the integration ofthe high-quality annotated data provided by Tictag, SafetyTech observed the following improvements in their safety video analytics system:

  1. Enhanced Detection Rates: The AI system's threat and safety violation detection rates increased by 35%, reducing risks for SafetyTech's clients.
  2. Reduced False Alarms: The number of false alarms dropped by 40%, increasing the system's reliability and user satisfaction.
  3. Accelerated Decision-Making: The improved AI system enabled safety and security professionals to make faster, data-driven decisions, reducing response times incritical situations.


The collaboration between Tictag and SafetyTech showcases the importance of high-quality data annotation in improving AI-powered safety video analytics systems. Through precise and accurate annotation, Tictag enabled SafetyTech to achieve their objectives, resulting in a safer and more secure environment for their clients.This case study demonstrates the critical role data annotation plays in advancing AI applications across various industries.