Video annotation is relatively difficult as the objects are continuously moving. From the process of classifying or categorizing objects and annotating them frame-by-frame to making them identifiable for machine learning or computer-vision-based models, the process is more complex than ever before.
Each item must be labeled or illustrated with cuboids, lines and splines, bounding boxes, or other annotation modes based on business requirements. The process mainly involves adding metadata to unlabeled video in order to train a machine learning algorithm.
Talk To Us
Your machine learning models can be trained with large volumes of accurately annotated video data. You can leverage technology to solve most of the problems themselves using a self-learning mechanism, thereby reducing your agent effort.
It bridges the semantic gap between high-level user’s need (human perception) and low-level feature (machine) description. By using relevant information and meta tags, we get the content based on relevant output.
Video annotation allows us to collect meaningful data based on business needs. By inputting relevant meta tags, we can retrieve the most relevant information with ease, thereby saving time, money, and resources.
Without a complete focus, it is not easy for the data annotation service to maintain high accuracy and quality levels. However, once done correctly, it can significantly reduce your throughput time and accelerate several processes.
Self-surveillance and relevant data collection options are available 24*7 with meta tags input, which makes the output more personalized and predictive. This ensures a quick turnaround time.
We deliver monitored and quality controlled annotated videos by professionals with experience in managing large enterprise volume.
