Optimizing the autonomous perception of vehicles through video annotation

In the automotive industry, the development of autonomous vehicles relies on the ability of embedded systems to understand their environment in real time. This requires very high quality video datasets, annotated with extreme precision.

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The mission

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Objective: create a training dataset for the detection and classification of road objects (vehicles, pedestrians, signs, traffic lights) from video streams. To achieve this objective, Innovatiana has deployed a dedicated approach including:

• Frame-by-frame annotation in bounding boxes and polygons to capture the dynamics of road scenes;
• Systematic quality control to ensure temporal and spatial consistency in annotations.

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The results

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• Extensively annotated video sequences to feed the perception algorithms;
• A significant improvement in obstacle detection and autonomous navigation in various conditions;
• An acceleration of the testing and validation phases of ADAS and autonomous systems.

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👉 Read our article on ADAS annotation : Learn how accurate video annotation enhances the intelligence of autonomous vehicles.