Traffic sign detection and recognition systems are essential components of Advanced Driver Assistance Systems and self-driving vehicles. In this contribution we present a vision-based framework which detects and recognizes traffic signs inside the attentional visual field of drivers. This technique takes advantage of the driver's 3D absolute gaze point obtained through the combined use of a front-view stereo imaging system and a non-contact 3D gaze tracker. We used a linear Support Vector Machine as a classifier and a Histogram of Oriented Gradient as features for detection. Recognition is performed by using Scale Invariant Feature Transforms and color information. Our technique detects and recognizes signs which are in the field of view of the driver and also provides indication when one or more signs have been missed by the driver.

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Visual saliency models imitate the attentive mechanism of the human visual system (HVS) to detect the objects that stand out from their neighbors in the scene. Some biological phenomena in HVS, such as contextual cueing effects, suggest that the contextual information of the whole scene does guide the attentive mechanism. The saliency value of each image patch is influenced by its visual (local) features as well as the contextual information of the whole scene. Modern saliency models are based on deep convolutional neural networks. Because the convolutional operators operate locally and use weight sharing, such networks inherently have difficulty capturing global and location-dependent features. In addition, these models calculate the saliency value pixel-wise using local features. Therefore, it is necessary to provide global features along with local features. In this regard, we propose two approaches for capturing the contextual information from the scene. In our first method, we introduce a shift-variant fully connected component to capture global and location-dependent information. Instead of using the native CNN of our base model, in our second method, we use a VGGNet to capture the global and context information of the scene. To show the effectiveness of our methods, we use them to extend the SAM-ResNet saliency model. To evaluate our proposed approaches, four challenging saliency benchmark datasets were used. The experimental results showed that our methods could outperform the existing state-of-the-art saliency prediction models.

Saliency computation models aim to imitate the attention mechanism in the human visual system. The application of deep neural networks for saliency prediction has led to a drastic improvement over the last few years. However, deep models have a high number of parameters which makes them less suitable for real-time applications. Here we propose a compact yet fast model for real-time saliency prediction. Our proposed model consists of a modified U-net architecture, a novel fully connected layer, and central difference convolutional layers. The modified U-Net architecture promotes compactness and efficiency. The novel fully-connected layer facilitates the implicit capturing of the location-dependent information. Using the central difference convolutional layers at different scales enables capturing more robust and biologically motivated features. We compare our model with state of the art saliency models using traditional saliency scores as well as our newly devised scheme. Experimental results over four challenging saliency benchmark datasets demonstrate the effectiveness of our approach in striking a balance between accuracy and speed. Our model can be run in real-time which makes it appealing for edge devices and video processing.