Success Story Manufacturing
German Automaker Labels 12M Objects per Annum from Street Videos
The proliferation of sensors and cameras fosters next-generation ecosystems such as smart cities, smart transportation, smart infrastructure, etc.
The value lies in accurately translating visual data into actionable insights in real-time. This leading German Automaker wanted to automate the labeling process for street videos with live footage from various daytime conditions and locations to identify objects such as vehicles, street lights, and pedestrians.
With our deep learning platform for visual data analysis, the customer was able to:
Manually labeling objects from street videos was leading to:
Protracted cycles Protracted cycles for object analysis
Increased Costs Increased cost due to higher manual labor
Human Errors Higher scope for human errors
Apexon brought onboard its Image and Video Analytics platform, a proprietary deep learning platform that speeds up analysis of visual data to enable:
Proactive prediction of objects seen in the video with deep learning algorithms such as EfficientDet, Resnet 50, Mask CNN, and the COCO weights
Identification of object location across all frames and annotation generation with object tracking algorithms and computer vision
Conversion of annotations to XMLs with an XML generator to calibrate pinhole camera position and tool resolution
Curation of tags and corresponding images with XML parser – a data generator tool
