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ETISEO Project

The ETISEO Project features a comprehensive video corpus designed for the evaluation of visual tracking and object recognition systems in challenging conditions. It includes realistic video sequences with varying lighting, occlusions, and diverse environments such as corridors, subway stations, and car parks. The dataset comprises multiple sequences showcasing different scenarios like vehicle parking, people entering/exiting vehicles, and pedestrian movements. With graduate difficulty levels and distinct contexts, this corpus aims to facilitate performance evaluation and comparison across algorithms.

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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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