Browsing by Author "Nabil, Ayman"

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    Detecting Abnormal Fish Behavior Using Motion Trajectories In Ubiquitous Environments
    (Elsevier Ltd, 2020-01) Anas, Omar; Wageeh, Youssef; Mohamed, Hussam El-Din; Fadl, Ali; El Masry, Noha; Nabil, Ayman; Atia, Ayman
    Monitoring fish farms as controlling water quality and abnormal fish behaviors inside fish pond are one of the most costly and difficult task to do for fish farmers. Fish farmers normally do these tasks manually, which requires them to dedicate lots of time and money. Way for detecting fish behaviors is presented in this paper by identifying the fish and analyzing their trajectories in a difficult water environment. First of all, we used an image enhancement algorithm to color-enhance water pictures and to enhance fish detection. We then used an algorithm for object detection to identify fish. Finally, we used a classification algorithm to detect fish abnormal behavior. Our aim is making an automated system that monitors the fish farm to reduce costs and time for the fish farmers and provide them with more efficient and easy ways to perform their operations
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    iKarate: Improving Karate Kata
    (Elsevier, 2020-01) Emad, Bassel; Atef, Omar; Shams, Yehya; El-Kerdany, Ahmed; Shorim, Nada; Nabil, Ayman
    Scopus
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    MSR-YOLO: Method to Enhance Fish Detection and Tracking in Fish Farms
    (Elsevier, 2020-02) Mohamed, Hussam El-Din; Fadl, Ali; Anas, Omar; Wageeh, Youssef; ElMasry, Noha; Nabil, Ayman; Atia, Ayman
    Tasks involving the monitoring of fish farms such as controlling fish ponds is one of the expensive and difficult tasks for fish farmers. Usually, fish farmers are doing these tasks manually which costs them time and money. We propose a system that automates the monitoring of the fish farm. This paper presents a technique to enhance the detection of fish and their trajectories in challenging water conditions. Firstly, we used image enhancement techniques to enhance unclear water images and to better identify fish. Then, we applied an object detection algorithm to detect fish. Finally, the detected objects’ coordinates are then used to extract features like count and trajectories. All experiments were done on our experimental setup. The technique showed promising results in regards to detection and tracking accuracy when applied