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Being aware of other traffic is a prerequisite for self-driving cars tooperate in the real world. In this paper, we show how the intrinsic featuremaps of an object detection CNN can be used to uniquely identify vehicles froma dash-cam feed. Feature maps of a pretrained `YOLO' network are used to create700 deep integrated feature signatures (DIFS) from 20 different images of 35vehicles from a high resolution dataset and 340 signatures from 20 differentimages of 17 vehicles of a lower resolution tracking benchmark dataset. TheYOLO network was trained to classify general object categories, e.g. classify adetected object as a `car' or `truck'. 5-Fold nearest neighbor (1NN)classification was used on DIFS created from feature maps in the middle layersof the network to correctly identify unique vehicles at a rate of 96.7\% forthe high resolution data and with a rate of 86.8\% for the lower resolutiondata. We conclude that a deep neural detection network trained to distinguishbetween different classes can be successfully used to identify differentinstances belonging to the same class, through the creation of deep integratedfeature signatures (DIFS).

"Who is Driving around Me?" Unique Vehicle Instance Classification using Deep Neural Features