Automatic Animal Detection from Kinect Sensed Images for Livestock Monitoring and Assessment

Zhu, Qiaoming; Ren, Jinchang; Barclay, David; McCormack, Samuel; Thomson, W. T.

doi:10.1109/cit/iucc/dasc/picom.2015.172

Cited by 34 publications

(33 citation statements)

References 4 publications

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“…Recently a fully automated weight estimation technique has been introduced to estimate a marked pig's weight individually (Kashiha et al, 2014b;Shi et al, 2016). Furthermore, approaches for pig live weight estimation by means of a Kinect camera have utilized infrared depth map images (Kongsro, 2014;Zhu et al, 2015).…”

Section: Live Weightmentioning

confidence: 99%

Implementation of machine vision for detecting behaviour of cattle and pigs

2017

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Section: Live Weightmentioning

confidence: 99%

Implementation of machine vision for detecting behaviour of cattle and pigs

2017

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“…In this study, we focus on video-based pig monitoring applications with non-attached (i.e., non-invasive) sensors [ 5 , 6 , 7 , 8 , 9 , 10 , 11 , 12 , 13 , 14 , 15 , 16 ]. Furthermore, we employ a top-view depth sensor [ 17 , 18 , 19 , 20 , 21 , 22 ] due to the practical difficulties presented in commercial farms where the light is turned off at night (i.e., light fluctuations, shadowing, cluttered backgrounds, varying floor status caused by urine/manure, etc.). In fact, we previously reported results for Kinect-based pig detection [ 22 ].…”

Section: Introductionmentioning

confidence: 99%

A Kinect-Based Segmentation of Touching-Pigs for Real-Time Monitoring

Choi

Seo

et al. 2018

Sensors

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Segmenting touching-pigs in real-time is an important issue for surveillance cameras intended for the 24-h tracking of individual pigs. However, methods to do so have not yet been reported. We particularly focus on the segmentation of touching-pigs in a crowded pig room with low-contrast images obtained using a Kinect depth sensor. We reduce the execution time by combining object detection techniques based on a convolutional neural network (CNN) with image processing techniques instead of applying time-consuming operations, such as optimization-based segmentation. We first apply the fastest CNN-based object detection technique (i.e., You Only Look Once, YOLO) to solve the separation problem for touching-pigs. If the quality of the YOLO output is not satisfied, then we try to find the possible boundary line between the touching-pigs by analyzing the shape. Our experimental results show that this method is effective to separate touching-pigs in terms of both accuracy (i.e., 91.96%) and execution time (i.e., real-time execution), even with low-contrast images obtained using a Kinect depth sensor.

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“…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%

“…Recently, low-cost depth cameras such as Kinect have been released. Compared with typical stereo-camera-based solutions, a Kinect can provide more accurate depth information at a much lower cost, without a heavy computational workload [ 39 , 40 , 41 , 42 , 43 ]. In principle, Kinect cameras can recognize whether pigs are lying or standing based on the depth data measured.…”

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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Automatic Animal Detection from Kinect Sensed Images for Livestock Monitoring and Assessment

Cited by 34 publications

References 4 publications

Implementation of machine vision for detecting behaviour of cattle and pigs

Implementation of machine vision for detecting behaviour of cattle and pigs

A Kinect-Based Segmentation of Touching-Pigs for Real-Time Monitoring

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

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