Change Detection in Crowded Underwater Scenes - Via an Extended Gaussian Switch Model Combined with a Flux Tensor Pre-segmentation

Radolko, Martin; Farhadifard, Fahimeh; Lukas, Uwe von

doi:10.5220/0006258504050415

Cited by 6 publications

(2 citation statements)

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“…The existing fish detection and tracking methods in the literature predominantly use conventional machine learning based approaches such as Histogram of Oriented Gradients (HOG), background subtraction, Gaussian Mixture Models, Kalman filter, Hungarian algorithm and Viola Jones based methods [8], [10], [14], [15], [34], [52], [53], [54], [55], [56], [56], [57], [58]. Kalman filter performs better when the motion of the objects are linear.…”

Section: B Object Tracking Methodsmentioning

confidence: 99%

Fish Tracking and Continual Behavioral Pattern Clustering Using Novel Sillago Sihama Vid (SSVid)

et al. 2023

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Aquaculture provides food security to many developing countries and enhances the socio-economic conditions of the fishermen. To enhance the productivity of the aquaculture, it is necessary to maintain stress free controlled eco-system for the fishes. For recognising the stress in fishes, behaviour analysis of fishes via tracking is imperative. Early detection of stress in fish facilitates fishermen to take precautionary measures promptly. Computer vision-based fish behaviour analysis of economically important fish species is challenging due to the lack of datasets, occlusions, rapid changes in swim directions etc. The present study proposes a multiple fish video dataset of an economically important species in a controlled environment, namely Sillago Sihama-Vid with accurate annotations. The study emulates the natural environment of Sillago Sihama in a large aquarium. This work proposes a novel fish tracking algorithm that incorporates swim direction information in addition to temporal, appearance, and spatial information. The inclusion of swim direction information reduces the number of identity switches. Comparative performance analysis of the proposed tracking algorithm with the conventional methods on the developed dataset highlights the performance efficiency. The proposed method has a clear performance improvement in MOTA, MOTP, IDSW and MT with respect to the other compared methods. The study also presents a novel unsupervised continual behaviour modelling strategy to model the evolving behaviours of the fishes. Further, interpretation of fish behaviour from the proposed behaviour modelling is performed to highlight the reliability of the proposed method. The significance of the proposed method is that, it is independent of training and labelled data. In addition, the method represents an innovative alternative to capture all the non observable behaviours of the fishes. The proposed tracking and behaviour modelling strategy act as a benchmark for developing algorithms to study fish behaviour via tracking. Finally, the dataset provides an opportunity for developing computer vision-based models to analyse the different behaviours of fish Sillago Sihama.INDEX TERMS Pattern analysis, fish behaviour analysis, fish tracking, fish trajectory dataset.

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Section: B Object Tracking Methodsmentioning

confidence: 99%

Fish Tracking and Continual Behavioral Pattern Clustering Using Novel Sillago Sihama Vid (SSVid)

et al. 2023

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“…In addition, the effects of various image quality enhancement techniques for underwater change detection on the segmentation algorithm have been investigated previously (Radolko et al, 2016;Radolko et al, 2017). Although research on CNN-based segmentation has not been conducted, experiments related to various existing segmentation algorithms have been reported (KaewTraKulPong and Bowden, 2001;Zivkovic, 2004;Zivkovic and Heijden, 2006;Radolko and Gutzeit, 2015).…”

Section: Introductionmentioning

confidence: 99%

PSS-net: Parallel semantic segmentation network for detecting marine animals in underwater scene

Kim

Park²

2022

Front. Mar. Sci.

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Marine scene segmentation is a core technology in marine biology and autonomous underwater vehicle research. However, it is challenging from the perspective of having a different environment from that of the conventional traffic segmentation on roads. There are two major challenges. The first is the difficulty of searching for objects under seawater caused by the relatively low-light environment. The second problem is segmenting marine animals with protective colors. To solve such challenges, in previous research, a method of simultaneously segmenting the foreground and the background was proposed based on a simple modification of the conventional model; however, it has limitations in improving the segmentation accuracy. Therefore, we propose a parallel semantic segmentation network to solve the above issues in which a model and a loss are employed to locate the foreground and the background separately. The training task to locate the foreground and the background is reinforced in the proposed method by adding an attention technique in a parallel model. Furthermore, the final segmentation is performed by aggregating two feature maps obtained by separately locating the foreground and the background.The test results using an open dataset for marine animal segmentation reveal that the proposed method achieves performance of 87%, 97.3%, 88%, 95.2%, and 0.029 in the mean intersection of the union, structure similarities, weighted F-measure, enhanced-alignment measure, and mean absolute error, respectively. These findings confirm that the proposed method has higher accuracy than the state-of-the-art methods. The proposed model and code are publicly available via Github1.

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Change Detection and Blob Tracking of Fish in Underwater Scenarios

Radolko

Farhadifard

Lukas

2019

Communications in Computer and Information Science

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Change Detection in Crowded Underwater Scenes - Via an Extended Gaussian Switch Model Combined with a Flux Tensor Pre-segmentation

Cited by 6 publications

References 0 publications

Fish Tracking and Continual Behavioral Pattern Clustering Using Novel Sillago Sihama Vid (SSVid)

Fish Tracking and Continual Behavioral Pattern Clustering Using Novel Sillago Sihama Vid (SSVid)

PSS-net: Parallel semantic segmentation network for detecting marine animals in underwater scene

Change Detection and Blob Tracking of Fish in Underwater Scenarios

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