Pedestrian Detection using Deep Learning-Matlab
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Pedestrian Detection using Deep Learning
Pedestrian detection is the task of detecting pedestrians from a camera.Pedestrian detection is an essential and significant task in any intelligent video surveillance system, as it provides the fundamental information for semantic understanding of the video footages. It has an obvious extension to automotive applications due to the potential for improving safety systems. Many car manufacturers offer this as an ADAS . The pedestrian detection network was trained by using images of pedestrians and non-pedestrians. This network is trained in MATLAB® by using the trainPedNet.m helper script. A sliding window approach crops patches from an image of size [64 32]. Patch dimensions are obtained from a heatmap, which represents the distribution of pedestrians in the images in the data set. It indicates the presence of pedestrians at various scales and locations in the images. In this example, patches of pedestrians close to the camera are cropped and processed. Non-Maximal Suppression (NMS) is applied on the obtained patches to merge them and detect complete pedestrians.
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