Optical Fish Tracking in Fishways using Neural Networks
One of the main issues in Computer Vision is to extract the movement of one or several points or objects of interest in an image or video sequence to conduct any kind of study or control process. Different techniques to solve this problem have been applied in numerous areas such as surveillance systems, analysis of traffic, motion capture, image compression, navigation systems and others, where the specific characteristics of each scenario determine the approximation to the problem. This paper puts forward a Computer Vision based algorithm to analyze fish trajectories in high turbulence conditions in artificial structures called vertical slot fishways, designed to allow the upstream migration of fish through obstructions in rivers. The suggested algorithm calculates the position of the fish at every instant starting from images recorded with a camera and using neural networks to execute fish detection on images. Different laboratory tests have been carried out in a full scale fishway model and with living fishes, allowing the reconstruction of the fish trajectory and the measurement of velocities and accelerations of the fish. These data can provide useful information to design more effective vertical slot fishways.
[1] Castro-Santos T, Haro A and Walk S. (1996). A passive integrated
transponder (PIT) tag system for monitoring fishways. Fisheries
research. 28(3):253-261
[2] Chang T-H and Gong S. (2001) Tracking Multiple People with a Multi-
Camera System, Proc. IEEE Workshop Multi-Object Tracking, with
ICCV -01, July 2001.
[3] Cheng HD, Jiang XH, Sun Y and Wang J. (2001). Color image
segmentation: advances and prospects. Pattern Recognition. 34(2001)
2259-2281
[4] Cheung S-C, Kamath C (2004) Robust Techniques for background
subtraction in urban traffic video. Visual Communications and Image
Processing, 5308(1):881-892
[5] Deng Z, Richmond CM, Guensch GR and Mueller RP. (2004). Study of
Fish Response Using Particle Image Velocimetry and High-Speed,
High-Resolution Imaging, Technical Report PNNL-14819
[6] Dong G and Xie M. (2005). Color clustering and learning for image
segmentation based on neural networks. IEEE transactions on neural
networks 16(4):925-936
[7] Duarte S, Reig L, Oca J and Flos R. (2004). Computerized imaging
techniques for fish tracking in behavioral studies. European Aquaculture
Society, 2004. p. 310
[8] Larinier, M. (2002). Biological factors to be taken into account in the
design of fishways, the concept of obstructions to upstream migrations.
Larinier M., Travade F. & porcher J.P. (eds.): "Fishways: biological
basis, design criteria and monitoring" Bull. Fr. Pêche Piscic. Suppl. 364:
28-38
[9] Moraís EF, Campos MFM, Padúa FLC and Carceroni RL. (2005).
Particle filter-based predictive tracking for robust fish count. XVIII
Brazilian Symposium on Computer Graphics and Image Processing,
SIBGRAPI05
[10] Moya F, Herrero V, Guerrero G. (1998). La aplicaci├│n de redes
neuronales artificiales (RNA) a la recuperaci├│n de la informaci├│n.
SOCADI yearbook of information and documentation 1998(2)147-164
[11] Puertas J, Pena L and Teijeiro T. (2004). An Experimental Approach to
the Hydraulics of Vertical Slot Fishways. Journal of Hydraulics
Engineering, ASCE 130(1). Jan 2004
[12] Ramanan, D, Forsyth, DA. (2003) Finding and tracking people from the
bottom up. Computer Vision and Pattern Recognition, 2003.
Proceedings. 2003 IEEE Computer Society Conference on.
[13] Stauffer C and Grimson WEL. (2000). Learning patterns of activity
using real-time tracking. IEEE Transaction on Pattern Analysis and
Machine Intelligence, 22(8):747-757, 2000.
[14] Smith P, Drummond T and Cipolla R. (2000). Motion segmentation by
tracking edge information over multiple frames. Proc. 6th ECCV, June
2000, Dublin, Ireland (II): 396-410.
[15] Steig TW, Iverson TK. (1998). Acoustic monitoring of salmonid density,
target strength, and trajectories at two dams on the Columbia River,
using a split-beam scanning system. Fisheries Research 35:43-53
[16] Subhashis B. (2008). Projective geometry, camera models and
calibration. IIT. Delhi. (Accessed: 20/03/2010).
http://www.cse.iitd.ernet.in/~suban/vision/geometry/index.html
[17] Tarrade L, Texier A, David L and M. Larinier (2008). Topologies and
measurements of turbulent flow in vertical slot fishways. Hydrobiologia
609(1): 177-188.
[18] Utsumi A and Ohya J. (2000) Multiple-Camera-Based Human Tracking
Using Non-Synchronous Observations, Proc. Asian Conf. Computer
Vision,pp. 1034-1039, Jan. 2000.
[19] Verikas A, Malmqvist K and Bergman L. (1997). Color image
segmentation by modular neural networks. Pattern Recognition Letters
18(1997):173-185
[20] Wu S, Rajaratman N and Katopodis C, (1999). Structure of flow in
vertical slot fishways. Journal of Hydraulic Engineering. 125 (4), 351-
360
[1] Castro-Santos T, Haro A and Walk S. (1996). A passive integrated
transponder (PIT) tag system for monitoring fishways. Fisheries
research. 28(3):253-261
[2] Chang T-H and Gong S. (2001) Tracking Multiple People with a Multi-
Camera System, Proc. IEEE Workshop Multi-Object Tracking, with
ICCV -01, July 2001.
[3] Cheng HD, Jiang XH, Sun Y and Wang J. (2001). Color image
segmentation: advances and prospects. Pattern Recognition. 34(2001)
2259-2281
[4] Cheung S-C, Kamath C (2004) Robust Techniques for background
subtraction in urban traffic video. Visual Communications and Image
Processing, 5308(1):881-892
[5] Deng Z, Richmond CM, Guensch GR and Mueller RP. (2004). Study of
Fish Response Using Particle Image Velocimetry and High-Speed,
High-Resolution Imaging, Technical Report PNNL-14819
[6] Dong G and Xie M. (2005). Color clustering and learning for image
segmentation based on neural networks. IEEE transactions on neural
networks 16(4):925-936
[7] Duarte S, Reig L, Oca J and Flos R. (2004). Computerized imaging
techniques for fish tracking in behavioral studies. European Aquaculture
Society, 2004. p. 310
[8] Larinier, M. (2002). Biological factors to be taken into account in the
design of fishways, the concept of obstructions to upstream migrations.
Larinier M., Travade F. & porcher J.P. (eds.): "Fishways: biological
basis, design criteria and monitoring" Bull. Fr. Pêche Piscic. Suppl. 364:
28-38
[9] Moraís EF, Campos MFM, Padúa FLC and Carceroni RL. (2005).
Particle filter-based predictive tracking for robust fish count. XVIII
Brazilian Symposium on Computer Graphics and Image Processing,
SIBGRAPI05
[10] Moya F, Herrero V, Guerrero G. (1998). La aplicaci├│n de redes
neuronales artificiales (RNA) a la recuperaci├│n de la informaci├│n.
SOCADI yearbook of information and documentation 1998(2)147-164
[11] Puertas J, Pena L and Teijeiro T. (2004). An Experimental Approach to
the Hydraulics of Vertical Slot Fishways. Journal of Hydraulics
Engineering, ASCE 130(1). Jan 2004
[12] Ramanan, D, Forsyth, DA. (2003) Finding and tracking people from the
bottom up. Computer Vision and Pattern Recognition, 2003.
Proceedings. 2003 IEEE Computer Society Conference on.
[13] Stauffer C and Grimson WEL. (2000). Learning patterns of activity
using real-time tracking. IEEE Transaction on Pattern Analysis and
Machine Intelligence, 22(8):747-757, 2000.
[14] Smith P, Drummond T and Cipolla R. (2000). Motion segmentation by
tracking edge information over multiple frames. Proc. 6th ECCV, June
2000, Dublin, Ireland (II): 396-410.
[15] Steig TW, Iverson TK. (1998). Acoustic monitoring of salmonid density,
target strength, and trajectories at two dams on the Columbia River,
using a split-beam scanning system. Fisheries Research 35:43-53
[16] Subhashis B. (2008). Projective geometry, camera models and
calibration. IIT. Delhi. (Accessed: 20/03/2010).
http://www.cse.iitd.ernet.in/~suban/vision/geometry/index.html
[17] Tarrade L, Texier A, David L and M. Larinier (2008). Topologies and
measurements of turbulent flow in vertical slot fishways. Hydrobiologia
609(1): 177-188.
[18] Utsumi A and Ohya J. (2000) Multiple-Camera-Based Human Tracking
Using Non-Synchronous Observations, Proc. Asian Conf. Computer
Vision,pp. 1034-1039, Jan. 2000.
[19] Verikas A, Malmqvist K and Bergman L. (1997). Color image
segmentation by modular neural networks. Pattern Recognition Letters
18(1997):173-185
[20] Wu S, Rajaratman N and Katopodis C, (1999). Structure of flow in
vertical slot fishways. Journal of Hydraulic Engineering. 125 (4), 351-
360
@article{"International Journal of Earth, Energy and Environmental Sciences:60308", author = "Alvaro Rodriguez and Maria Bermudez and Juan R. Rabuñal and Jeronimo Puertas", title = "Optical Fish Tracking in Fishways using Neural Networks", abstract = "One of the main issues in Computer Vision is to extract the movement of one or several points or objects of interest in an image or video sequence to conduct any kind of study or control process. Different techniques to solve this problem have been applied in numerous areas such as surveillance systems, analysis of traffic, motion capture, image compression, navigation systems and others, where the specific characteristics of each scenario determine the approximation to the problem. This paper puts forward a Computer Vision based algorithm to analyze fish trajectories in high turbulence conditions in artificial structures called vertical slot fishways, designed to allow the upstream migration of fish through obstructions in rivers. The suggested algorithm calculates the position of the fish at every instant starting from images recorded with a camera and using neural networks to execute fish detection on images. Different laboratory tests have been carried out in a full scale fishway model and with living fishes, allowing the reconstruction of the fish trajectory and the measurement of velocities and accelerations of the fish. These data can provide useful information to design more effective vertical slot fishways.
", keywords = "Computer Vision, Neural Network, Fishway, Fish Trajectory, Tracking", volume = "4", number = "8", pages = "384-7", }
