DIPLODOC Road Stereo Sequence is a labelled road sequence of 865 image pairs taken from a stereo camera mounted on a moving vehicle. We used these images to benchmark our stereo algorithm for road and obstacle detection. The road region was manually segmented and it is proposed as ground truth for performance evaluation of road detection algorithms. The sequence is the composition of five sub-sequences, each of them presenting very different traffic and road conditions: from highway-like roads to urban scenarios with crossroads, parking lots and complex environments; from congested traffic to completely free roads. The images were captured on July, 16 2004, about 11 a.m., near Trento, ITALY. The following map shows where the sub-sequences were taken.
The acquisition device was a Videre Design MEGA-D stereo camera pair installed near the rear-view mirror. The sequence is 15 fps, 320x240, color. The images are saved in the lossless format PNG without any pre-processing.
The stereo pair is calibrated with the SVS software and the camera parameters are included in the downloadable file. Here an example of left and right images in the sequence (frame no. 202):
The definition of road given to the labeller was: "everywhere a car could drive without going up a step". The ground truth for an image is saved as a set of polygons. Some of them represent ideal road regions, some of them represent objects that occlude portions of the road. From these regions it is possible to compute the road region actually visible in the considered image. The following figure presents an example (frame no. 202), with one road region and two occluding regions. The image on the right is the derived visible road region.
Disclaimer: DIPLODOC road stereo sequence, with ground truth data, camera parameters and its documentation is provided for research or academic purposes only. Publications and works that use this dataset should please refer to the following technical report:
M. Zanin, S. Messelodi, C. M. Modena, "DIPLODOC road stereo sequence", FBK Technical Report Nr. 164010
DIPLODOC_RoadStereoSequenceDataset.zip includes: gtseq002836.tgz (214 MB), README.txt, roadseqgt.pdf
some info about the project
DIPLODOC - DIstributed Processing of LOcal Data for On-line Car services - project goal is to design and develop a system based on a distributed architecture where intelligent vehicles communicate with a remote traffic control center.
Each vehicle integrates different technologies to provide more comfort and driver safety. Speech recognition and synthesis techniques are used to interact with the user. Computer vision and image understanding are applied to the extraction of traffic parameters and to accident avoidance by detection and recognition of obstacles on the road using on-board stereo camera. Wireless telecommunication is used to send and receive traffic data and route planning information to/from the control center. The practical result of the project will be a simulation of the traffic control center and a demonstrative vehicle equipped with vision, speech and telecommunication devices.
The DIPLODOC architecture has been designed in order to satisfy four services, defined as follows:
xFCD (eXtended Floating Car Data): the vehicles act as mobile probes for the collection of remote data. Each vehicle transmits its position provided by the satellite positioning system and data coming from the engine control unit and from the on-board vision system. Moreover, still images are sent to a specialized vision module hosted in the center for a deeper analysis. The system at the traffic control center exploits and integrates the information coming from the vehicle fleet in order to deduce the local traffic conditions.
xEC (eXtended Emergency Call): the user can activate manually or by voice an emergency call to the control center. The emergency call allows the operator in the traffic control center to be timely informed about the on-board situation. Extended information, like video sequences and audio recording, are sent in addition to the position data of standard emergency call.
DRP (Dynamic Route Planning): the driver can request by voice a route plan for the travel from the traffic center: how to arrive at a desired destination starting from the current vehicle position, considering also information about traffic levels on the possible paths.
FOR (Front Obstacle Recognition): this service aims at warning the driver when pedestrians, vehicles or obstacles are in close proximity to the driver’s intended path, using information coming from the on-board vision sensors, the vehicle sensor data, and possibly from environmental conditions or driver activity to modulate the alarm level.
Duration: from 1 April 2002 to 31 March 2005 (3y)
ITC (now FBK) - TeV unit (coordinator) and SHINE unit
CRF (Centro Ricerche FIAT)
University of Trento
Project partially funded by Provincia Autonoma di Trento under "Fondo Unico" program
C. Corridori, D. Giordani, P. Lombardi, S. Messelodi, C.M. Modena, M. Zanin, "An in-vehicle vision system for dangerous situation detection", Conferenza Italiana sui Sistemi Intelligenti (CISI 04),Perugia, Italy, 15-17 September 2004
C. Corridori and M. Zanin, “High Curvature Two-Clothoid Road Model Estimation,” in Proceedings of IEEE Conference on Intelligent Transportation Systems (ITSC 2004), Washington DC, USA, October 3-6, 2004.
P. Lombardi, M. Zanin, and S. Messelodi, "Switching Models for Vision-Based On-Board Road Detection", 8th International IEEE Conference on Intelligent Transportation Systems (ITSC 2005), Vienna, Austria, September 13-16, 2005
P. Lombardi, M. Zanin, and S. Messelodi, "Unified Stereovision for Ground, Road, and Obstacle Detection", IEEE Intelligent Vehicles Symposium (IV 2005), Las Vegas, Nevada, USA, June 6-8, 2005
M. Zanin, "Vision-based Road Recognition and Obstacle Detection for Intelligent Vehicles", PhD thesis, University of Trento, Italy, 2006
M. Zanin, "Localization of ahead vehicles with on-board stereo cameras", 14th International Conference on Image Analysis and Processing (ICIAP 2007), September 10-14, 2007
S. Messelodi, C.M. Modena, M. Zanin, F. Granelli, F.G.B. De Natale, E. Betterle, and A. Guarise, "Intelligent Extended Floating Car Data Collection", Expert Systems with Applications, Volume 36, Issue 3, Part 1, April 2009, Pages 4213-4227 [doi]