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ETISEO Project

ETISEO Project. Video corpus dedicated to the first evaluation. ETISEO Project. To capture realistic video sequences with graduate difficulties: lighting variations, occlusions … covering predefined scenarios. ETISEO Project. Video Providers Corpus Data description

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ETISEO Project

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  1. ETISEO Project • Video corpus • dedicated to • the first evaluation

  2. ETISEO Project To capturerealisticvideo sequences with graduate difficulties: • lighting variations, • occlusions … • covering predefined scenarios.

  3. ETISEO Project • Video Providers • Corpus Data description • Video contents demonstration

  4. Video providers Several data providers offeringlarge diversity. • Differentsites : • corridor, • sub-way, • - building entry, • - apron, • - car park. • Differentcontexts : • indoor / outdoor • single / multi-camera • big / small overlap area • visible / IR • actors / real scenes

  5. Video Sensors Video providers : Silogic Toulouse-Blagnac Airport France Apron

  6. Video providers : Silogic Apron Multi-view

  7. Video providers : INRETS Building Entry Multi-view Car Park

  8. Video providers : CEA Corridor Car street

  9. Video providers : CEA Geometry : IR/Colour calibration Estimation of an homographic matrix between the 2 images (sensors close and aligned)

  10. Video providers : RATP Subway Real scene

  11. Corpus data • Video Providers • Corpus Data description • Video contents demonstration

  12. Priority Sequences • Evaluation risk: • That participants process only part of the dataset. • Resulting in dispersed metrics. • No real evaluation or comparison being possible

  13. Priority Sequences • Solution: • Create some mandatory sequences. • Participants should process at least these sequences. • Need some feedback to select these priority sequences. See the questionnaire.

  14. Corpus data For each topic, the set contains sequences with graduate difficulty.

  15. Corpus data • Covered scenarios : • vehicle parking • people getting in & out from a vehicle • people walking • people crossing • person entering a building • person entering a room • person meeting • abandoned baggage

  16. Corpus data • Summary : • indoor / outdoor sequences, • 8 scenarios, • graduate difficulty, • 20 sequences, • 37 video clips, • 5.2 Go of data.

  17. Corpus diffusion • Video format : • sequence of jpeg/bmp images for colour images • sequence of TIFF and jpeg images for IR. • Timestamp : • Image name contains the timestamp relative to the start of the sequence. • Calibration : • Provide as • - a calibration matrix used to generate 3D • - couples of 2D / 3D points.

  18. Corpus diffusion • Structure: Contains the sequence ETI-VS2-RD-32 ETI-VS2-RD-32-C1 Camera folders C1-Format.xml ETI-VS2-RD-32-C1-00h00m36s000-0000.jpeg ETI-VS2-RD-32-C1-….jpeg ETI-VS2-RD-32-C1-00h02m40s719-1559.jpeg ETI-VS2-RD-32-C2 C2-Format.xml ETI-VS2-RD-32-C2-00h00m36s000-0000.jpeg ETI-VS2-RD-32-C2-….jpeg ETI-VS2-RD-32-C2-00h02m40s723-1559.jpeg

  19. Corpus diffusion • Structure: • Each video resource will be store in a unique folder using the following convention: • ETI-VSindexdataset-Type-sequenceindex • Example: • ETI-VS2-RD-32

  20. Corpus diffusion • C1-format.xml : • <?xml version="1.0" encoding="UTF-8"?> • <sequence name=”ETI-VS1-BC-32”> • <properties> • <format>JPEG</format> could be JPEG,TIFF… • <width>720</width> • <height>576</height> • <depth>24</depth> • <frames>1560</frames> # of frames (start=0) • <inter-frame-ms>80000</inter-frame-ms> average time in ms between frames • <maxsize>65232</maxsize> maximum image size • <camera>C1</camera> camera id • <sensor>color</sensor> color, IR, greyscale • </properties> • </sequence>

  21. ETISEO Project • Video Providers • Corpus Data description • Video content demonstration

  22. Video demonstration • Silogic / Apron: • Type : multi-view, played and real scenes. • Precision evaluation : scenes with real 3D ground truth. • person walking on the zone. C1, C2, C3, C4 • vehicle moving on the zone. C1, C2, C3, C4 • Real scenes : vehicles moving on the apron with large variation of conditions (outside environment). • GPU arrival « normal conditions ». C1 , C2 • GPU arrival «  reflections ». C1 , C2 • GPU arrival «  shadows » C1, C2

  23. Video demonstration • INRETS / parking & building entry. • Type : multi-view, played scenes. • Scenes : vehicles parking, people entering/leaving cars, entering/leaving the building. Large variation of conditions (outside environment). • ETI-VS1-BE-18: C1,C2,C3,C4 • ETI-VS1-BE-19: C4 • ETI-VS1-BE-20: C1,C2,C3,C4

  24. Video demonstration • INRETS / parking & building entry. • Type : multi-view, played scenes. • Scenes : Sequence ETI-VS1-BE-20 will be provided with different compression levels: • BMP, JPEG and MPEG.

  25. Video demonstration • CEA / parking & building entry. • Type : single-view but visible/IR couple. Played scenes. • Road : vehicles and people. • ETI-VS1-RD-14: C1, C2 • ETI-VS1-RD-15: C1 , C2 • ETI-VS1-RD-16: C1 , C2 • ETI-VS1-RD-17: C1 , C2 • Corridor : People crossing, abandoned luggage. • ETI-VS1-BC-11 C1 , C2 • ETI-VS1-BC-12 C1 , C2 • ETI-VS1-BC-13 C1, C2

  26. Video demonstration • RATP / subway. • Type : single-view. Played scenes mixed with real environment. • Corridor : People crossing, abandoned luggage. • ETI-VS1-MO-07 • ETI-VS1-MO-08 • ETI-VS1-MO-09 • Railway platform : People crossing, abandoned luggage. • ETI-VS1-MO-10

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