Abstract
Studies on tracking fishes have become a popular research endeavour in recent years. Many methods have been used to track fishes by integrating microchips in fishes, using infra-red cameras, image processing and motion sensor. The use of particle filter in the process of tracking has been widely used by researchers. Particle filters is used to track people, fluid movement and animals. In this paper, the particle filter algorithm is improved to track multiple fish in a fish tank. The aim is to identify every fish trajectories and fish target location for further analysis. The main challenge is to ensure that the correct fish are tracked and the algorithm manages to identify specific fish even if they overlaps with each another. The objective of the study is to improve the existing particle filter to track multiple fish in a single fish tank. The improved algorithm contains an additional cache which stores the object’s position to estimate the next potential move of the fish. The result is evaluated by comparing existing algorithm without the enhancement with the improved algorithm. Besides, suggestions in improving the particle filter will also be discussed in this paper.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Wong, P.L., Osman, M.A., Talib, A.Z., Yahya, K.: Modelling of Fish Swimming Patterns Using an Enhanced Object Tracking Algorithm. Frontiers in Computer Education, 585–592 (2012)
Muizz, W.A., Osman, M.A., Talib, A.Z., Yahya, K.: Framework For Modelling of Fish Behaviour Through Fish Swimming Patterns. In: Ocean & Coastal Observation: Sensors and Systems (OCOSS 2010). Congress Center Le Quartz, Brest (2010)
Mariño, C., Ortega, M., Barreira, N., Penedo, M.G., Carreira, M.J., González, F.: A Simple Implementation of the Condensation Algorithm. Computer Methods and Programs in Biomedicine (2011)
Monteiro, J.B.O., de Andrade Silva, J., Machado, B.B., Pistori, H., Odakura, V.: Multiple Mice Tracking using a Combination of Particle Filter and K-Means. In: Computer Graphics and Image Processing, SIBGRAPI 2007, Minas Gerais, pp. 173–178 (2007)
Williams, R., Purser, J.: Application of the Particle Filter to Tracking of Fish in Aquaculture Research. In: Digital Image Computing: Techniques and Applications (DICTA), Sch. of Comput. & Inf. Syst., Univ. of Tasmania, Hobart, TAS, pp. 457–464 (2008)
Isard, M., Blake, A.: CONDENSATION – conditional density propagation for visual tracking. International Journal on Computer Vision 29 (1998)
Meier, E.B., Ade, F.: Tracking cars in range images using the CONDENSATION algorithm. In: Proceedings of the 1999 IEEE/IEEJ/JSAI International Conference on Intelligent Transportation Systems, pp. 129–134 (1999)
Koller-Meier, E., Ade, F.: Tracking Multiple Objects Using the Condensation Algorithm. Journal of Robotics and Autonomous Systems 34, 93–105 (2001)
Yui, M.L., Ross, B.J., Darrell, W.L.: A Novel Appearance Model and Adaptive Condensation Algorithm for Human Face Tracking. Colourado State University, Fort Collins, CO 80523 (2008)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2014 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Lee, W.P., Osman, M.A., Talib, A.Z., Ogier, JM., Yahya, K. (2014). Tracking Multiple Fish in a Single Tank Using an Improved Particle Filter. In: Jeong, H., S. Obaidat, M., Yen, N., Park, J. (eds) Advances in Computer Science and its Applications. Lecture Notes in Electrical Engineering, vol 279. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-41674-3_114
Download citation
DOI: https://doi.org/10.1007/978-3-642-41674-3_114
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-41673-6
Online ISBN: 978-3-642-41674-3
eBook Packages: EngineeringEngineering (R0)