title: Automated detection of feeding strikes by larval fish using continuous high-speed digital video: a novel method to extract quantitative data from fast, sparse kinematic events.
abstract: Using videography to extract quantitative data on animal movement and kinematics constitutes a major tool in biomechanics and behavioral ecology. Advanced recording technologies now enable acquisition of long video sequences encompassing sparse and unpredictable events. Although such events may be ecologically important, analysis of sparse data can be extremely time-consuming and potentially biased; data quality is often strongly dependent on the training level of the observer and subject to contamination by observer-dependent biases. These constraints often limit our ability to study animal performance and fitness. Using long videos of foraging fish larvae, we provide a framework for the automated detection of prey acquisition strikes, a behavior that is infrequent yet critical for larval survival. We compared the performance of four video descriptors and their combinations against manually identified feeding events. For our data, the best single descriptor provided a classification accuracy of 77-95% and detection accuracy of 88-98%, depending on fish species and size. Using a combination of descriptors improved the accuracy of classification by ∼2%, but did not improve detection accuracy. Our results indicate that the effort required by an expert to manually label videos can be greatly reduced to examining only the potential feeding detections in order to filter false detections. Thus, using automated descriptors reduces the amount of manual work needed to identify events of interest from weeks to hours, enabling the assembly of an unbiased large dataset of ecologically relevant behaviors.
authors.affiliation: Department of Mathematics and Computer Science, The Open University of Israel, 1 University Road, P.O.B. 808, Raanana 43107, Israel.
authors.forename: Eyal
authors.initials: E
authors.lastname: Shamur
authors.affiliation: Department of Physics, Faculty of Exact Sciences, Tel Aviv University, Tel Aviv 69978, Israel.
authors.forename: Miri
authors.initials: M
authors.lastname: Zilka
authors.affiliation: Department of Mathematics and Computer Science, The Open University of Israel, 1 University Road, P.O.B. 808, Raanana 43107, Israel.
authors.forename: Tal
authors.initials: T
authors.lastname: Hassner
authors.affiliation: Department of Zoology, Faculty of Life Sciences, Tel Aviv University, Tel Aviv 69978, Israel The Inter-University Institute for Marine Sciences, POB 469, Eilat 88103, Israel.
authors.forename: Victor
authors.initials: V
authors.lastname: China
authors.affiliation: School of Mechanical Engineering, Faculty of Engineering, Tel Aviv University, Tel Aviv 69978, Israel.
authors.forename: Alex
authors.initials: A
authors.lastname: Liberzon
authors.affiliation: Department of Zoology, Faculty of Life Sciences, Tel Aviv University, Tel Aviv 69978, Israel The Inter-University Institute for Marine Sciences, POB 469, Eilat 88103, Israel holzman@post.tau.ac.il.
authors.forename: Roi
authors.initials: R
authors.lastname: Holzman
mesh_terms: D000818:Animals; D001331:Automation; D001696:Biomechanical Phenomena; D005247:Feeding Behavior / Q000502:physiology*; D005399:Fishes / Q000254:growth & development / Q000502:physiology*; D007814:Larva / Q000502:physiology; D008018:Life Cycle Stages; D009055:Mouth / Q000502:physiology; D062211:Spatio-Temporal Analysis; D013223:Statistics as Topic / Q000379:methods*; D013997:Time Factors; D014741:Video Recording
keywords: Automated classification; Feeding kinematics; High-speed video; Machine learning