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Environment perception is a critical enabler for automated driving systems since it allows a comprehensive understanding of traffic situations. We propose a method based on an end-to-end convolutional neural network that can reason simultaneously about the location of objects in the image and their orientations on the ground plane. The same set of convolutional layers is used for the different tasks...
In this paper the authors propose a pedestrian detection system based on discrete features in infrared images. Unique keypoints are searched for in the images around which a descriptor, based on the histogram of the phase congruency orientation, is extracted. These descriptors are matched with defined regions of the body of a pedestrian. In case of a match, it creates a region of interest in the image,...
In this paper a new contrast invariant descriptor for human detection in long-wave infrared images is proposed. It exploits local information histogram of orientations of phase coherence. Contrast in infrared images depends on the temperature of the object and the background, which makes gradient based descriptors less robust, especially in daylight conditions. The objective is to obtain a scale,...
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