This code accompanies the paper: Eyal Shamur, Tal Hassner, Miri Zilka, Victor China, Alex Liberzon, Roi Holzman, "Identification of feeding strikes by larval fish from continuous high-speed digital video": The paper provides a framework for automated detection of prey acquisition strikes from a long video of foraging larvae, a behavior that is infrequent yet critical for larval survival. Supervised learning models of local volume video descriptors are used to generate the detection by classification framework. We compared the performance of the proposed framework against manually identified feeding events. Our results indicate that the effort required by an expert to manually label videos can be reduced from weeks to hours, enabling the assembly of large, unbiased dataset of ecologically relevant behaviors.

If you find our models or code useful, please add suitable reference to our paper in your work. The SOFTWARE provided in this page is provided "as is", without any guarantee made as to its suitability or fitness for any particular use. It may contain bugs, so use of this tool is at your own risk. We take no responsibility for any damage of any sort that may unintentionally be caused through its use. The purpose of this repository is to assist readers in reproducing our results on feeding strikes detections as described in our work.

"CodeInfo.txt": In order to have an easy and friendly introduction to our code, we added detailed explanation of the code structure and external lib used with the code. Please read the "codeInfo.txt" file to have a quick understanding of our code. Please note that the code is mainly written with Matlab but also contains executable files made by VisualStudioC++.