We developed an end-to-end pipeline for brake light transition detection based on cognitive theories of anomaly-detection and model based systems engineering principles. Inspired by cognitive theory, we decompose the visual input stream into the submodalities color, shape, intensity and motion which is closely coupled with a graphical model. A memory module that contains priors is populated by exploitation of knowledge from specifications and simulation. High-fidelity 3D-Simulations have been created to populate motion memory, whereas low-fidelity 3D-projections have been used to create geometric priors. We captured real world sequences containing RGB images, GPS- and inertial measurement-data and annotated them with boundingboxes for cars and their tail and brakelight states, which are used to evaluate the proposed pipeline.
Anomaly detection for automotive visual signal transition estimation
01.10.2017
487774 byte
Aufsatz (Konferenz)
Elektronische Ressource
Englisch
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