Abstract
In this paper, we design a framework called detection-tracking fish counting (DTFC) for overhead videos of fish moving in shallow water. DTFC includes three steps, fish tracking, counting zone generation and fish counting. In this work, we adopt two deep learning framework FasterRCNN and DeepSORT as our detector and tracker, respectively. Conventional fish counting methods need to predefine the counting zone, and as a result, they become very inefficient. In DTFC, we use the tracking to refine the generated trajectories and automatically generate the counting zone. For the detection of small objects, we divide the overhead image into four sub-images and enlarge the sub-images for detector training. Our experimental results show that DTFC is very suitable for fish tracking and counting, and may provide a valuable resource for fisheries management.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
