Detection, tracking and sizing of fish of in data from DIDSON multibeam sonars

1. Detection, tracking and sizing of fish of in data from DIDSON multibeamsonars Helge Balk1, Torfinn Lindem1, Jan Kubečka2 1 Department of Physics, University of Oslo, PO.Box.1048. Blindern, NO-0317 Oslo, Norway email: helge.balk@fys.uio.no, Torfinn.lindem.@fys.uio.no 2 Biology Centre of Czech Academy of Sciences, Institute of Hydrobiology, Na sadkach 7, CZ 37005 Ceske Budejovice, Czech Republic. e-mail: kubecka@hbu.cas.cz,

2. Introduction Conclusion Detection methods CFD AND DIDSON Echogram approach tracking 3D approach Inc.Video methods

3. Placing Norway on the map University of Oslo No Biological institute Cz

4. Ourmaininterest • As usual to find out abot the fish • How many • How big • What are they doing

5. Equipment that may be used Coda OctopusEchoscope Resons-Seabat DIDSON Simrad SM2000 Split beam Simrad MS70

6. DIDSON • Dual frequency Identification SONnar • Developed for military underwater tasks like diver night vision and mine searching • Become popular for fish studies • Identification ability • Can see pictures of the fish. • Fish size from geometry, not from TS

7. Ouraim • Develoop a target detector for DIDSON data • Can vi use the Cross Filter Detector CFD develooped for ordinary echogram • If not, can we optimise it to fit the DIDSON data • Or is there something to learn from the video world

8. Dual-Frequency Identification Sonar (DIDSON)

9. DIDSON problems • Low snr, • Low dynamic span, • Not calibrated, • Not veldefined sample volume • Only x,z, but no y position information

10. DIDSON inside

11. Examples of data

12. Introduction Detection methods Conclusion CFD AND DIDSON Tracking Aim, material and methods Echogram approach 3D approach

13. Detectiontheory - methods • Edgebased • Gradient operators • Linking Edge • Thresholding • Constant, • Addaptive, • Stastistical • Relaxation • If this is a fish pixel, then…

14. a Comparator Filter 1 Evaluator b Filter 2 c Variance Cross Filter Detector (CFD) Traces Filterdirection Signal a Signal b Signal c Combine Evaluator Input echogram

15. CFD –Addaptivethresholding Main challenge: Find the optimal threshold signal threshold

16. Detectionmethods How to fit the Crossfilter to video like data? Can we learn something from the video world? Foreground filter Echogram Comparator Evaluator Crossfilter detector Background filter variance Comparator Evaluator Video Common video processing Background Modelling

17. Backgroundmodelling. – the most important part. • Recursive • Approximated median • Kalmann filter • Mixture of Gausians • Non recursive • Previous picture • Median • Linear predictive • Nonparametric Comparator Evaluator Video Common video processing Background Modelling

18. Backgroundmodelling. – the most important part. • Three best • 1 Mixture of Gausians • 2 Median • 3 Approximated median • Not much difference • App. Median much faster and simpler than the others Ching , Cheung and Kamath found Sen-Ching S. Cheung and Chandrika KamathCenter for Applied Scientic Computing Lawrence Livermore National Laboratory, Livermore, CA 94550

19. Comparator Comparator Evaluator Video Background Modelling Common video processing

20. Evaluator • Morfological filter • Recognise fish on size and shape • May use higher order statistics • Connect parts of targets Comparator Evaluator Video Common video processing Background Modelling

21. Introduction Summary Detection methods CFD AND DIDSON Echogram approach Tracking 3D approach Inc.Video methods

22. Echogram approach Gain 96-Ch Amplitude Detector Multi beam-viewer Echogram generator Multi  1 beam Amp-Echogram

23. Generate echograms and apply the Cross-Filter How to combine many beams into one ? a) Mean echogram • At each range bin extract mean values from a selected number of beams. Like an ordinary transducer with controllable opening angle b) Max Intensity • At each range bin, select the sample from the beam with highest intensity

24. Data recorded by Debby Burwen Generating Echograms from multi beam Many beams  1 beam b) Pick the beam with strongest intensity a) Averaging a number of beams 10x12 deg

25. Echogram approach Testing the CFD on many to 1 beam echograms

26. Echogram approach works well until density becomes too high Echogram approach We want to push the density limit

27. Introduction The original Cross filter Summary CFD AND DIDSON Tracking Aim, material and methods Echogram approach 3D approach

28. 3D approach Adding a third dimension • Work directly on the multi beam data • Want to detect more than one target in the same range bin time range range width time 2d-trace 3d-trace

29. 3D approach We added the beam dimension to the filters Beam. nr Running window operators Ping 2D 3D Ping Range Range New DDF

30. Test foreground filter operator size Frame Beam Range 5 1 3 3 5 1 5 3 1

31. Test Background filter operator size 1 Frame Beam Range 1 5 15 25 1

32. Testing cross filter on a small trout in Fisha River Max Intensity echogram

33. Forefilt 3 x 3 x 3 CFD with filters and threshold Back filt 3 x 3 x 3 Threshold Offset=20

34. Evaluator can take away unwanted targets

36. Introduction Summary Detection methods CFD AND DIDSON Echogram approach Tracking 3D approach Inc.Video methods

37. Extended the background filter with an approximated median operator (N. McFarlane and C. Schoeld 1995) Q ddf

38. And extended the comparator with alternatives Threshold detection a If ( a - b )>T ) Foreground b Background

39. Forefilt 3 x 3 x 3 Background subtraction Back filt 3 x 3 x 3 App.Median Threshold Offset=20

40. Introduction Summary Detection methods CFD AND DIDSON Echogram approach Tracking 3D approach Inc.Video methods

41. The initial idea was to detect traces directly by clustering Cluster of overlapping fish pictures ( Work well in the echogram approach )

42. Center of gravity track But data often showed traces split up in individual fish pictures Tracker needed for fast fish Clustering worked for big slow fish

43. Fish center line predictor Special predictor can be made for multi beam data In addition to traditional predictors are available such as Alpha Beta and Kalman Special predictor can be formed from the DIDSON fish picture

44. Introduction Summary Detection methods CFD AND DIDSON Echogram approach Tracking 3D approach Inc.Video methods

45. Summary Foreground filter Echogram Comparator Evaluator Crossfilter detector Background filter variance Comparator Evaluator Video Background Modelling Common video processing Tracker Evaluator 3D-Foreground filter Comparator DIDSON Best method Background Modelling

46. Best method for moving targets Summary • Needed in most cases • Need for variouspredictorsdependingon data ( a - b )>T ) a DIDSON Tracker 3D-Foreground filter Comparator Evaluator b Background Modelling 3D better than 2D Optimise on improving foreground Improvedforeground signal Approximated Median

47. Run demo nowif time

48. And thatwas it! Thanks for theattention! Questions? Introduction Summary Detection methods CFD AND DIDSON Echogram approach Tracking 3D approach Inc.Video methods