Robust human detection within a highly dynamic aquatic environment in real time

Eng, How-Lung; Wang, Junxian; Wah, A.; Yau, Wei-Yun

doi:10.1109/tip.2006.871119

Cited by 34 publications

(2 citation statements)

References 34 publications

(53 reference statements)

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“…One such example is given by How-Lung et al (2006) who developed a system which can monitor people in an aquatic environment. The application acts as a virtual life guard for a swimming pool.…”

Section: Research Based Vision Systems That Determine Behaviourmentioning

confidence: 99%

A brief review of the application of machine vision in livestock behaviour analysis

Tscharke

Banhazi²

2016

JAI

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It is desirable to increase the frequency between livestock welfare assessments to enhance problem identification and consumer confidence in livestock welfare management. However, animal welfare is difficult to monitor in practice, due to the inefficiencies involved in manually documenting and determining, animal behaviour, social interaction and health condition of large numbers of animals. Furthermore, the effectiveness of a welfare assessment relies on the intuition of the observer which may vary considerably between assessors. Hence, this review investigates the application of machine vision systems to recognise and monitor the behaviour of animals in a quantitative manner. Behaviour-recognition concepts, techniques, and current behaviour monitoring systems are reviewed. Findings indicate that further research is required to develop systems that can monitor the behaviour and welfare of animals' more efficiently and effectively in commercially realistic environments. Introduction: Livestock Behaviour and Vision SystemsMeasuring and assessing the behaviour of livestock is important as it can be used to indicate their welfare status (Dutta et al., 2015;Porto et al., 2014). Behaviour is formed from an animal's continuous interaction with its environment (Figure 1). Animal behaviour is a response to an internal stimuli (physiological) such as hunger or an external stimuli such as climate. If the goal of the behaviour is not, or cannot, be achieved the animal may change its behaviour or physiological response (Busse et al., 2015). There is potential for welfare problems to arise when there are inadequate environmental enrichments to support the behavioural needs of livestock.It has been argued that the husbandry methods used in intensive livestock production have resulted in the deprivation of some naturally occurring behaviours (Fraser, 1983). Producers have a strong interest in maintaining the welfare of their livestock from both economic and ethical perspectives. As often proactive welfare management has a positive effect on a farm's production efficiency and the quality and marketability of the end product (Kashiha et al., 2013).The observation processes currently used to measure livestock behaviour are subjective, as farm workers perform the welfare assessment. The worker's involvement in these tasks are necessary and therefore increase the demand on the labour and costs associated with monitoring animal behaviour. These factors have potential to influence the level of attention each animal receives (Oczak et al., 2013;Pereira et al., 2013). Thus, behavioural measurements are open for interpretation and have potential to be overlooked.Given the problems mentioned above, this review investigates the application of machine vision systems to recognise and monitor the behaviour of animals in a quantitative manner. Specifically in this article, behaviour-recognition concepts, techniques, and current behaviour monitoring systems are reviewed. Applying machine vision systems to the behaviour recognition taskTo over...

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Section: Research Based Vision Systems That Determine Behaviourmentioning

confidence: 99%

A brief review of the application of machine vision in livestock behaviour analysis

Tscharke

Banhazi²

2016

JAI

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“…This method is extended in [7] to simulate the dynamic outdoor aquatic background with rapid illumination changes by modelling the background as dynamic homogeneous regions. Bayesian frameworks [8] and Hidden Markov Models (HMM) [6] were also employed to improve the recognition accuracy. Besides the background subtraction, optical flow can also be utilized in this area.…”

Section: Related Workmentioning

confidence: 99%

Swimmer Localization from a Moving Camera

Sha

Lucey

Morgan³

et al. 2013

2013 International Conference on Digital Image Computing: Techniques and Applications (DICTA)

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At the highest level of competitive sport, nearly all performances of athletes (both training and competitive) are chronicled using video. Video is then often viewed by expert coaches/analysts who then manually label important performance indicators to gauge performance. Stroke-rate and pacing are important performance measures in swimming, and these are previously digitised manually by a human. This is problematic as annotating large volumes of video can be costly, and timeconsuming. Further, since it is difficult to accurately estimate the position of the swimmer at each frame, measures such as stroke rate are generally aggregated over an entire swimming lap. Vision-based techniques which can automatically, objectively and reliably track the swimmer and their location can potentially solve these issues and allow for large-scale analysis of a swimmer across many videos. However, the aquatic environment is challenging due to fluctuations in scene from splashes, reflections and because swimmers are frequently submerged at different points in a race. In this paper, we temporally segment races into distinct and sequential states, and propose a multimodal approach which employs individual detectors tuned to each race state. Our approach allows the swimmer to be located and tracked smoothly in each frame despite a diverse range of constraints. We test our approach on a video dataset compiled at the 2012 Australian Short Course Swimming Championships.

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Unsupervised Video Analysis for Counting of Wood in River during Floods

Ali¹,

Tougne²

2009

Lecture Notes in Computer Science

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Robust human detection within a highly dynamic aquatic environment in real time

Cited by 34 publications

References 34 publications

A brief review of the application of machine vision in livestock behaviour analysis

A brief review of the application of machine vision in livestock behaviour analysis

Swimmer Localization from a Moving Camera

Unsupervised Video Analysis for Counting of Wood in River during Floods

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