Real-time recognition of sows in video: A supervised approach

Khoramshahi, Ehsan; Hietaoja, Juha; Valros, Anna; Yun, Je Moon; Pastell, Matti

doi:10.1016/j.inpa.2014.07.002

Cited by 13 publications

(11 citation statements)

References 11 publications

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“…To the best of our knowledge, the study that uses Background Subtraction method as object detection method and Artificial Neural Networks as object classification method is rare. There is only one related study [24] that combines these two methods. However, the study concludes that the accuracy of the proposed system is 84.6%.…”

Section: Introductionmentioning

confidence: 99%

A Hybrid Background Subtraction and Artificial Neural Networks for Movement Recognition in Memorizing Quran

Prabuwono¹,

Maula²,

Usino³

et al. 2018

ijacsa

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Movement change beyond the duration of time and the variations of object appearance becomes an interesting topic for research in computer vision. Object behavior can be recognized through movement change on video. During the recognition of object behavior, the target and the trace of an object in a video must be determined in the sequence of frames. To date, the existence of object on a video has been widely used in different areas such as supervision, robotics, agriculture, health, sports, education, and traffic. This research focuses on the field of education by recognizing the movement of Quantum Maki Quran memorization through a video. The purpose of this study is to enhance the existing computer vision technique in detecting the Quantum Maki Quran memorization movement on a video. It combines the Background Subtraction method and Artificial Neural Networks; and evaluates the combination to optimize the system accuracy. Background Subtraction is used as object detection method and Back propagation in Artificial Neural Networks is used as object classification. Nine videos are obtained by three different volunteers. These nine videos are divided into six training and three testing data. The experimental result shows that the percentage of accuracy system is 91.67%. It can be concluded that there are several factors influencing the accuracy, such as video capturing factors, video improvements, the models, feature extraction and parameter definitions during the Artificial Neural Networks training.

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Section: Introductionmentioning

confidence: 99%

A Hybrid Background Subtraction and Artificial Neural Networks for Movement Recognition in Memorizing Quran

Prabuwono¹,

Maula²,

Usino³

et al. 2018

ijacsa

View full text Add to dashboard Cite

show abstract

“…presented by commercial farms. Table 1 summarizes the topview-based pig monitoring results introduced recently [ 9 , 10 , 11 , 12 , 13 , 14 , 15 , 16 , 17 , 18 , 19 , 20 , 21 , 22 , 23 , 24 , 25 , 26 , 27 , 28 , 29 , 30 , 31 , 32 , 33 , 34 , 35 , 36 , 37 , 38 , 39 , 40 , 41 , 42 , 43 ]. Two-dimensional gray-scale or color information has been used to detect a single pig in a pen or a specially built facility (i.e., in “constrained” environments) [ 9 , 10 , 11 ].…”

Section: Introductionmentioning

confidence: 99%

“…Thus, a monitoring system based on a gray-scale or RGB camera cannot detect objects in low- to no-light conditions. Although some monitoring results at night have been reported using infrared cameras [ 34 , 35 , 36 ], problems caused by a cluttered background cannot be perfectly solved. Although some researchers have utilized a thermal camera to resolve the cluttered background problem [ 37 ], this is an expensive solution for large-scale farms.…”

Section: Introductionmentioning

confidence: 99%

Depth-Based Detection of Standing-Pigs in Moving Noise Environments

Kim

Chung

Choi

et al. 2017

Sensors

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In a surveillance camera environment, the detection of standing-pigs in real-time is an important issue towards the final goal of 24-h tracking of individual pigs. In this study, we focus on depth-based detection of standing-pigs with “moving noises”, which appear every night in a commercial pig farm, but have not been reported yet. We first apply a spatiotemporal interpolation technique to remove the moving noises occurring in the depth images. Then, we detect the standing-pigs by utilizing the undefined depth values around them. Our experimental results show that this method is effective for detecting standing-pigs at night, in terms of both cost-effectiveness (using a low-cost Kinect depth sensor) and accuracy (i.e., 94.47%), even with severe moving noises occluding up to half of an input depth image. Furthermore, without any time-consuming technique, the proposed method can be executed in real-time.

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“…Some of the ANN applications in recent years have been in livestock-based research: dairy cattle (Grzesiak et al, 2010), sheep (Kominakis et al, 2002;Tahmoorespur and Ahmadi, 2012) and pigs Wongsriworaphon et al, 2015). The performance of classifiers has a significant effect on machine vision outputs (Pourreza et al, 2012), and the feed-forward neural network is one of the most powerful classifiers, which could be fast enough and acceptable for many processes (Khoramshahi et al, 2014). The MLP network is a feed-forward network model which, with its simplicity, has the ability to provide good approximations and has been designed to function well in modelling data that are not linearly separable (Hong, 2012).…”

Section: Introductionmentioning

confidence: 99%

A new approach for categorizing pig lying behaviour based on a Delaunay triangulation method

Nasirahmadi

Hensel

Edwards

et al. 2017

Animal

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Machine vision-based monitoring of pig lying behaviour is a fast and non-intrusive approach that could be used to improve animal health and welfare. Four pens with 22 pigs in each were selected at a commercial pig farm and monitored for 15 days using top view cameras. Three thermal categories were selected relative to room setpoint temperature. An image processing technique based on Delaunay triangulation (DT) was utilized. Different lying patterns (close, normal and far) were defined regarding the perimeter of each DT triangle and the percentages of each lying pattern were obtained in each thermal category. A method using a multilayer perceptron (MLP) neural network, to automatically classify group lying behaviour of pigs into three thermal categories, was developed and tested for its feasibility. The DT features (mean value of perimeters, maximum and minimum length of sides of triangles) were calculated as inputs for the MLP classifier. The network was trained, validated and tested and the results revealed that MLP could classify lying features into the three thermal categories with high overall accuracy (95.6%). The technique indicates that a combination of image processing, MLP classification and mathematical modelling can be used as a precise method for quantifying pig lying behaviour in welfare investigations.

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Real-time recognition of sows in video: A supervised approach

Cited by 13 publications

References 11 publications

A Hybrid Background Subtraction and Artificial Neural Networks for Movement Recognition in Memorizing Quran

A Hybrid Background Subtraction and Artificial Neural Networks for Movement Recognition in Memorizing Quran

Depth-Based Detection of Standing-Pigs in Moving Noise Environments

A new approach for categorizing pig lying behaviour based on a Delaunay triangulation method

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