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ETISEO

ETISEO. François BREMOND ORION Team, INRIA Sophia Antipolis, France. Fair Evaluation. Unbiased and transparent evaluation protocol Large participation Meaningful evaluation. Tasks evaluated. GT & Metrics are designed to evaluate tasks all along the video processing chain:

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ETISEO

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  1. ETISEO François BREMOND ORION Team, INRIA Sophia Antipolis, France

  2. Fair Evaluation • Unbiased and transparent evaluation protocol • Large participation • Meaningful evaluation

  3. Tasks evaluated • GT & Metrics are designed to evaluate tasks all along the video processing chain: • Task 1: Detection of physical objects, • Task 2: Localisationof physical objects, • Task 3: Classification of physical objects, • Task 4: Tracking of physical objects, • Task 5: Event recognition.

  4. Matching Computation To evaluate the matching between a candidate result and a reference data, we may use following distances: • D1-The Dice coefficient: Twice the shared, divided by the sum of both intervals: 2*card(RDC) / (card(RD) + card(C)). • D2-The overlapping: card(RDC) / card(RD). • D3-Bertozzi and al. metric: (card(RDC))^2 / (card(RD) * card(C)). • D4-The maximum deviation of the candidate object or target according to the shared frame span: Max { card(C\RD) / card(C), card(RD\C) / card(RD) }. RD C

  5. metrics (1) T1- DETECTION OF PHYSICAL OBJECTS OF INTEREST C1.1 Number of physical objects C1.2 Number of physical objects using their bounding box T2- LOCALISATION OF PHYSICAL OBJECTS OF INTEREST • C2.1 Physical objects area (pixel comparison based on BB) • C2.2 Physical object area fragmentation (splitting) • C2.3 Physical object area integration (merge) • C2.4 Physical objects localisation • 2D and 3D • Centroïd or bottom centre point of BB

  6. metrics (2) T3- TRACKING OF PHYSICAL OBJECTS OF INTEREST • C3.1 Frame-To-Frame Tracking: Link between two frames • C3.2 Number of object being tracked during time • C3.3 Detection time evaluation • C3.4 Physical object ID fragmentation • C3.5 Physical object ID confusion criterion • C3.6 Physical object 2D trajectory • C3.7 Physical object 3D trajectory T4- CLASSIFICATION OF PHYSICAL OBJECTS OF INTEREST C4.1 Object Type over the sequence C4.2 Object classification per type C4.3 Time Percentage Good Classification card{ RDC, Type(C) = Type(RD) } / card(RDC) T5- EVENT RECOGNITION C5.1 Number of Events recognized over the sequence C5.2 Scenario parameters

  7. Metric Evaluation • Distance for matching groundtruth and algorithms results • Similar measures: D1, D2, D3, D4. • Few main metrics measure general trends • Discriminant and meaningful • Detection M1.2.1: CNumberObjectsBoundingBox • Localization M2.4.3: CCentroid2DLocalisationPix. • Tracking M3.3.1: CtrackingTime • Object Classification M4.1.3: CobjectTypeOverSequenceBBoxID • Event Recognition M5.1.2: CNumberNamedEvents

  8. Metric Evaluation (cont’d) • Secondary metrics: • Complementary information • Pixel-based (M2.1.1) versus object-based (M1.2.1) metrics • Potential algorithm errors. • Example: M3.3.1 complemented (eg., about stability) by M3.2.1, M3.4.1 and M3.5.1. • Non-informative Metrics: • Add noise to the evaluation or non-discriminative • Example: M1.1.1 CNumberObjects gives the object number per frame without position information. • The same for M4.1.1 and M5.1.1.

  9. Evaluation on ETI-VS2-BE-19-C1

  10. Global Results: Video • Remarks: • For similar scenes, very dissimilar results! • For different scenes, results can spread over a large range or concentrate in a narrow range.

  11. Detection of Physical Objects (ETI-VS2-BE-19-C1.xml) M1.1.1: NumberObjects M1.2.1: NumberObjectsBoundingBoxD1

  12. Detection of Physical Objects (ETI-VS2-BE-19-C1.xml) M1.2.1:NumberObjectsBoundingBoxD1 M2.1.1: ObjectsArea

  13. Detection of Physical Objects (ETI-VS2-BE-19-C1.xml) M2.2.1: SplittingD5 M2.3.1: MergingD2

  14. Summary on Detection of Physical Objects • Main metric measures: • Detection M1.2.1: CNumberObjectsBoundingBox • Problems: static objects, contextual objects, background, masks… • Advantages: objects vs pixels, large objects and bounding boxes • Secondary metrics: • M2.1.1 (area): indication on the precision and handling shadows • Split/Merge measures (M2.2.1, M2.3.1): • Advantage: indicate potential merge • Inconvenients: threshold-dependent, non-detected objects not taken into account

  15. Localisation of Physical Objects

  16. Localisation of Physical Objects(ETI-VS2-BE-19-C1.xml) M2.3.1: MergingD2 M2.4.3: Centroid2DLocalisationPixD1

  17. Localisation of Physical Objects(ETI-VS2-BE-19-C1.xml) M2.4.1: Centroid2DLocalisationD1 M2.4.3: Centroid2DLocalisationPixD1

  18. Localisation of Physical Objects(ETI-VS2-BE-19-C1.xml) M2.4.2.: Centroid3DLocalisationD1

  19. Summary on Localisation of Physical Objects • M2.4.1, M2.4.2, M2.4.3, main metrics: • Problems: low utilisation of 3D info and calibration • Good performance: good precision on reliable TP (handling shadow and merge) • Advantages: complementary to the Detection; normalised, pixel or meter metrics

  20. Tracking of Physical Objects

  21. Tracking of Physical Objects(ETI-VS2-BE-19-C1.xml) M3.2.1: NumberObjectTrackedD1 M3.3.1: TrackingTime

  22. Tracking of Physical Objects(ETI-VS2-BE-19-C1.xml) M3.4.1: PhysicalObjectIdFragmentation M3.5.1: PhysicalObjectIdConfusion

  23. Tracking of Physical Objects(ETI-VS2-BE-19-C1.xml) M3.6.1: PhysicalObject2DTrajectories M2.4.1: Centroid2DLocalisationD1

  24. Summary on Tracking of Physical Objects • M3.3.1, main metric: • Problems: propagation of detection errors • Advantages: good global overview • M3.2.1, secondary metric: • Good performance: consistent TP over time for few TPs • Problems: not taking into account of complete FN • Fragmentation/confusion (M3.4.1, M3.5.1): • Advantage: indicate potential ID switching • Inconvenients: not discriminative; favoring under-detection (few IDs); over-detection (multiple IDs)

  25. Object Classification

  26. Object Classification(ETI-VS2-BE-19-C1.xml) M4.1.1: ObjectTypeOverSequence M4.1.1b: ObjectTypeOverSequenceBoundingBoxD1 Subtype

  27. Object Classification(ETI-VS2-BE-19-C1.xml) M4.1.3: ObjectTypeOverSequenceBoundingBoxIdD1 M4.1.2: ObjectTypeOverSequenceBoundingBoxD1

  28. Object Classification

  29. Object Classification

  30. Summary on Object Classification • M4.1.2, M4.1.3, same main metrics: • Problems: low classification of subtypes (doors, bikes, bags), favoring a few good quality TPs. • Advantage: reliable. • M4.1.1 (without BBox): • Inconvenients: wrong evaluation result in case of double errors (classified noise and FN) • Advantage: indicate potential double errors.

  31. Event Recognition

  32. Event Recognition(ETI-VS2-BE-19-C1.xml)

  33. Event Recognition(ETI-VS2-BE-19-C1.xml)

  34. Event Recognition(ETI-VS2-BE-19-C1.xml) M5.1.1: NumberEvents M5.1.2: NumberNamedEventsD1

  35. Summary on Event Recognition • M5.1.2 (with time), main metrics: • Problems: lack of understanding of ground truth definition • Advantages: good global overview per scenario type. • M5.1.1, secondary metric: • Problems: not taking into account of occurrence time

  36. Evaluation Results

  37. Evaluation on ETI-VS2-BE-19-C4

  38. Detection of Physical Objects (ETI-VS2-BE-19-C4.xml) M1.2.1: NumberObjectsBoundingBoxD1 M2.1.1: ObjectsArea

  39. Tracking of Physical Objects (ETI-VS2-BE-19-C4.xml) M3.3.1.D1: TrackingTime M3.2.1: NumberObjectTrackedD1

  40. Event Recognition (ETI-VS2-BE-19-C4.xml) M5.1.2: NumberNamedEventsD1 M5.1.1: NumberEvents

  41. Evaluation Results

  42. Evaluation on ETI-VS2-MO-1-C1

  43. Detection of Physical Objects (ETI-VS2-MO-1-C1.xml) M1.2.1: NumberObjectsBoundingBoxD1 M2.1.1: ObjectsArea

  44. Tracking of Physical Objects (ETI-VS2-MO-1-C1.xml) M3.3.1.D1: TrackingTime M3.2.1: NumberObjectTrackedD1

  45. Event Recognition (ETI-VS2-MO-1-C1.xml) M5.1.1: NumberEvents M5.1.2: NumberNamedEventsD1

  46. Evaluation Results

  47. Evaluation on ETI-VS2-RD-6-C7

  48. Detection of Physical Objects (ETI-VS2-RD-6-C7.xml) M1.2.1: NumberObjectsBoundingBoxD1 M2.1.1: ObjectsArea

  49. Detection of Physical Objects: Reference Data Filtering (ETI-VS2-RD-6-C7.xml: M1.2.1 - NumberObjectsBoundingBoxD1) No filtering With filtering

  50. Detection of Physical Objects: Reference Data Filtering (ETI-VS2-RD-6-C7.xml: M2.1.1 - ObjectsArea) No filtering With filtering

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