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Objects Motion Detection in Domain-adapted Assisted Driving

The modern Advanced Driver Assistance Systems (ADAS) contributed to reduce road accidents due to the driver’s inexperience or unexpected scenarios. ADAS technologies allow the intelligent monitoring of the driving scenario. Recently, estimation of the visual saliency i.e. the part of the visual scene in which the driver put high visual attention has received significant research interests. This work makes further contributions to video saliency investigation for automotive applications. The difficulty to collect robust labeled data as well as the several features of the driving scenarios require the usage of such domain adaptation methods. A new approach to Gradient-Reversal domain adaptation in deep architectures is proposed. More in detail, the proposed pipeline enables an intelligent identification and segmentation of the motion salient objects in different driving scenarios and domains. The performed test results confirmed the effectiveness of the overall