TR2017-145

Barcode: Global Binary Patterns for Fast Visual Inference



We present Barcode, a global binary descriptor for images captured from a vehicle-mounted camera with two applications: localization and turn classification. Barcode characterizes an image by encoding the distribution of vertical lines into a binary descriptor: in each vertical stripe of an image, if any vertical line exists the corresponding bit is set to 1, otherwise 0. For localization, our approach uses a database of geolocated images, each having its Barcode precomputed during a preprocessing stage. In the run time, we first generate the binary descriptor for each image and then use the descriptor to find the location in the database via Hamming distance metric. For turn classification, we train a deep neural network that uses a set of Barcodes from consecutive images to classify turns (left, right, straight, and stationary). We show that Barcode extraction can be done at 100-1000 Hz, localization at 10 kHz, and turn classification at 1 kHz. We show compelling experimental results on KITTI dataset and other sequences captured near Harvard and Purdue campuses.