Improved particle filter algorithm combined with culture algorithm for collision Caenorhabditis elegans tracking.

In order to address the issue of tracking errors of collision Caenorhabditis elegans, this research proposes an improved particle filter tracking method integrated with cultural algorithm. The particle filter algorithm is enhanced through the integration of the sine cosine algorithm, thereby facilitating uninterrupted tracking of the target C. elegans. Furthermore, the cultural algorithm is employed to facilitate recognition of the target C. elegans following a collision. In addition, this method integrates the concepts of down-sample and marking to reduce the average processing time of the image. Ultimately, the experiment was conducted on two strains of C. elegans of six ages. The experimental results demonstrate that the proposed method can accurately identify the target worm in the post-collision stage. The proposed method has the potential to be utilized in the field of worm tracking, offering a novel method into the acquisition of collision C. elegans behavior.