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Issue No.05 - May (2014 vol.36)
pp: 1012-1025
Andreas Geiger , MPI for Intell. Syst., Tubingen, Germany
Martin Lauer , Inst. of Meas. & Control, Karlsruhe Inst. of Technol., Karlsruhe, Germany
Christian Wojek , MPI for Inf., Saarbrucken, Germany
Christoph Stiller , Inst. of Meas. & Control, Karlsruhe Inst. of Technol., Karlsruhe, Germany
Raquel Urtasun , Toyota Technol. Inst. at Chicago, Chicago, IL, USA
ABSTRACT
In this paper, we present a novel probabilistic generative model for multi-object traffic scene understanding from movable platforms which reasons jointly about the 3D scene layout as well as the location and orientation of objects in the scene. In particular, the scene topology, geometry, and traffic activities are inferred from short video sequences. Inspired by the impressive driving capabilities of humans, our model does not rely on GPS, lidar, or map knowledge. Instead, it takes advantage of a diverse set of visual cues in the form of vehicle tracklets, vanishing points, semantic scene labels, scene flow, and occupancy grids. For each of these cues, we propose likelihood functions that are integrated into a probabilistic generative model. We learn all model parameters from training data using contrastive divergence. Experiments conducted on videos of 113 representative intersections show that our approach successfully infers the correct layout in a variety of very challenging scenarios. To evaluate the importance of each feature cue, experiments using different feature combinations are conducted. Furthermore, we show how by employing context derived from the proposed method we are able to improve over the state-of-the-art in terms of object detection and object orientation estimation in challenging and cluttered urban environments.
INDEX TERMS
video signal processing, image sequences, object detection, object tracking, road traffic, traffic engineering computing,cluttered urban environments, 3D traffic scene understanding, movable platforms, probabilistic generative model, multiobject traffic scene understanding, 3D scene layout, object location, object orientation, scene topology, scene geometry, traffic activities, video sequences, visual cues, vehicle tracklet, vanishing points, semantic scene labels, scene flow, occupancy grid, object detection,Roads, Vehicles, Layout, Three-dimensional displays, Semantics, Splines (mathematics), Hidden Markov models,Robotics, Autonomous vehicles, Scene Analysis, Image Processing and Computer Vision,3D scene layout estimation, 3D scene understanding, autonomous driving
CITATION
Andreas Geiger, Martin Lauer, Christian Wojek, Christoph Stiller, Raquel Urtasun, "3D Traffic Scene Understanding From Movable Platforms", IEEE Transactions on Pattern Analysis & Machine Intelligence, vol.36, no. 5, pp. 1012-1025, May 2014, doi:10.1109/TPAMI.2013.185
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