A deep CNN is used for vehicle detection, orientation, and 3D location tasks, beating out current standards for these tasks. With self-driving cars around the corner, having models which can effectively identify other vehicles on the road is paramount.
Deep MANTA Shows Fantastic Results with Vehicle Detection on KITTI Benchmark
Post · Mar 23, 2017 18:47 · Share on Twitter
In this paper, we present a novel approach, called Deep MANTA (Deep Many-Tasks), for many-task vehicle analysis from a given image. A robust convolutional network is introduced for simultaneous vehicle detection, part localization, visibility characterization and 3D dimension estimation. Its architecture is based on a new coarse-to-fine object proposal that boosts the vehicle detection. Moreover, the Deep MANTA network is able to localize vehicle parts even if these parts are not visible. In the inference, the network's outputs are used by a real time robust pose estimation algorithm for fine orientation estimation and 3D vehicle localization. We show in experiments that our method outperforms monocular state-of-the-art approaches on vehicle detection, orientation and 3D location tasks on the very challenging KITTI benchmark.