Real-time fish tracking system based on edge computing for marine farming
Scarce fish dataset for aquaculture,an unstable tracking algorithm,and high costs all hinder the technique from being deployed.To solve these problems,a high-performance and real-time fish tracking system was built based on three aspects:fish behavior data,the fish tracking algorithm,and the edge computing platform.In terms of data,the pomfret tracking dataset was collected and labeled on pomfret activity video from the breeding environment.A detection-based fish tracking algorithm named FishTrack was proposed,which enhanced the object association strategy to reduce the tracking lost caused by pose change and long-time occlusion.The FishTrack was deployed on the lightweight edge platform enabling high performance with low computational costs.The results show that FishTrack achieves the tracking accuracy of 72.95%and reduces identification switch of ByteTrack by 83%.Moreover,the tracking system works with a running speed of 7.40 frame/s,which is suitable for real-time tracking under production environment.
fish tracking systemdeep learningedge computingmultiple object trackingobject detection
万方数据
维普
