Underwater Target Tracking via 3D Convolutional Networks

Lai, Yi-Chung; Huang, Renjie; Kuo, Yen-Chun; Tsao, Chun-Yu; Wang, Jung-Hua; Chang, Chung‐Cheng

doi:10.1109/iea.2019.8715217

Cited by 6 publications

(2 citation statements)

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“…Hence coordinates and posture code of 5 +1 will be recorded in the thread of 1 . If no object matching the fish 1 was found at the next frame, then coordinates and posture code previously recorded will be used to predict [1,2] the most likely path of the target fish for (t+1) th through (t+v) th frames, where v is heuristically set to 5. For example, in Fig.…”

Section: A Trackingmentioning

confidence: 99%

“…Deep learning techniques can do end-to-end detection of instances of semantic objects such as fish without specifically defining features, and are typically built on convolutional neural networks (CNN) [1,2]. Based on our previous work [3] employing deep learning Faster-rcnn [4] as an object detector to implement the tracking task for measuring the moving speed of fish, this paper presents a real-time solution for the problem of detecting anomalous behaviors for underwater fish.…”

Section: Introductionmentioning

confidence: 99%

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Anomalous Behaviors Detection for Underwater Fish Using AI Techniques

et al. 2020

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Anomalous events detection in real-world video scenes is a challenging problem owing to the complexity of anomaly and the untidy backgrounds and objects in the scenes. Although there are already many studies on dealing with this problem using deep neural networks, very little literature aims for real-time detection of the anomalous behavior of fish. This paper presents an underwater fish anomalous behavior detection method by combining deep learning object detection, DCG (Directed Cycle Graph), fish tracking, and DTW (Dynamic Time Warping). The method is useful for detecting the biological anomalous behavior of underwater fish in advance so that early countermeasures can be planned and executed. Also, through post-analysis it is possible to access the cause of diseases or death, so as to reduce unnecessary loss, facilitate precision breeding selection, and achieve ecological conservation education as well. A smart aquaculture system incorporating the proposed method and IoT sensors allows extensive data collection during the system operation in various farming fields, thus enabling to develop optimal culturing conditions, both are particularly useful for researchers and the aquaculture industry.

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Section: A Trackingmentioning

confidence: 99%