Cuboid Annotation Services in Video Annotation - Price: Rs. 0

In the field of computer vision, video annotation plays a critical role in training artificial intelligence systems to perceive, analyze, and respond to real-world environments. Among the different annotation techniques, cuboid annotation is essential for creating precise 3D representations of objects within videos. Unlike simple bounding boxes, cuboids capture an object’s length, width, and height, making them vital for applications that require depth perception and spatial awareness.
What is Cuboid Annotation?
Cuboid annotation involves marking objects in a video with 3D bounding boxes. This technique enables AI models to understand not only the position of an object but also its volume and orientation in space. It is commonly used in industries such as autonomous driving, robotics, logistics, and surveillance, where accurate distance and size estimation are crucial for decision-making.
The Process of Cuboid Annotation in Video Annotation
1. Data Collection
The foundation of accurate annotation begins with diverse and representative video data. At GTS.AI, datasets are manually collected across a wide range of environments and conditions. This includes capturing data from different geographic regions, ensuring ethnic and gender diversity, and spanning various age groups. Videos may include urban traffic, indoor retail spaces, or industrial warehouses, depending on the client’s project needs.
2. Annotation
Once the raw videos are prepared, cuboid annotation is applied to highlight objects of interest in three dimensions. Skilled annotators carefully label vehicles, pedestrians, machinery, or other relevant elements, frame by frame. This step ensures that the AI system can track object movement, depth, and orientation over time.
3. Quality Check (QC)
Every annotation project undergoes a rigorous quality control process. Multiple levels of review are conducted to identify and correct errors or inconsistencies. Rework is performed whenever necessary, ensuring the final annotated dataset meets strict accuracy requirements.
4. Data Cleaning
Before delivery, annotated video datasets are thoroughly cleaned. Redundant, incomplete, or low-quality data is removed, and only relevant, high-precision annotations are retained. This guarantees that the training datasets are reliable and free from noise, enhancing AI model performance.
Why Cuboid Annotation Matters
Cuboid annotation enriches AI models with spatial intelligence. For instance, autonomous vehicles can detect the distance and trajectory of pedestrians, while warehouse robots can identify and handle packages of varying dimensions. This higher level of precision directly contributes to safer, smarter, and more efficient AI systems.
GTS.AI’s Commitment to Standards
At GTS.AI, cuboid annotation in video projects is executed with strict adherence to global data security and privacy regulations, including GDPR and HIPAA. The company is ISO certified (9001:2015 and 27001:2013), reflecting its dedication to both quality management and information security. By integrating diversity in datasets, following industry-leading QC processes, and applying meticulous data cleaning, GTS.AI delivers annotation services that support accurate, ethical, and compliant AI development.