Video Tracking to Monitor Turkey Welfare

Ju, Shengtai; Erasmus, Marisa A.; Reibman, Amy R.; Zhu, Fengqing

doi:10.1109/ssiai49293.2020.9094604

Cited by 5 publications

(9 citation statements)

References 12 publications

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“…Table 1 shows the results of tracker evaluation. As can be seen from the results, our proposed method in this paper outperforms our previous tracker [9] by a significant amount, in both short-term and long-term tracking. Due to its high turkey tracking accuracy, the new tracker also shows promising results in tracking turkey heads.…”

Section: Evaluation Of Trackersmentioning

confidence: 61%

“…Regarding turkey reidentification after highly-deformable actions, there are a total of six instances of wing flapping and fast walking in our two 3-min evaluation clips. Our proposed tracker successfully reidentified the turkeys in five of six instances, whereas [9] and [7] only succeeded in two of six instances.…”

Section: Evaluation Of Trackersmentioning

confidence: 94%

“…Cosine distance is calculated in the feature space to match each new detection to an existing tracked object. Compared to [9], we added Kalman filtering to predict the locations of turkeys based on past measurements. We also added a feature descriptor to extract turkey features to maintain their identities.…”

Section: Turkey Trackermentioning

confidence: 99%

“…We detail the turkey and head trackers and their performance, and add the challenging behaviors of preening and pecking to the eating, drinking, and walking detection that we considered in [6]. Our turkey tracker relies on DeepSort [7], which performs significantly better for our application than the CSRDCF tracker [8] we explored in [9]; however, both trackers require modifications to become sufficiently accurate to facilitate acceptable behavior identification in our application.…”

Section: Introductionmentioning

confidence: 99%

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Turkey Behavior Identification Using Video Analytics And Object Tracking

Ju¹,

Erasmus

Zhu

et al. 2021

2021 IEEE International Conference on Image Processing (ICIP)

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In this paper, we propose a method to identify behavior of experimental turkeys by automatically analyzing video recordings. Monitoring turkey health during production is crucial for improved turkey production. Turkey health can be reflected through their common behavior, and changes in the frequency and duration of their behavior can be used to detect sick turkeys early. Video recordings can be manually annotated to assist identifying turkey behaviors, but this is both time consuming and labor intensive. In this paper, we monitor and detect changes in turkey behavior using video analytics. Behaviors of interest include eating, drinking, preening, and pecking. Identifying these behaviors requires accurate estimates of turkeys' and turkey heads' locations. Reidentification of each turkey is crucial after significant shape deformation such as wing flapping and fast walking. Therefore, our system integrates a state-of-the-art turkey tracker and a head tracker with a behavior identification module to identify turkey behavior. Results demonstrate that our system is effective and accurate at estimating the spatial location of turkeys and their heads, and identifying all behaviors of interest with high recall.

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Section: Evaluation Of Trackersmentioning

confidence: 61%

Section: Evaluation Of Trackersmentioning

confidence: 94%

Section: Turkey Trackermentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Turkey Behavior Identification Using Video Analytics And Object Tracking

Ju¹,

Erasmus

Zhu

et al. 2021

2021 IEEE International Conference on Image Processing (ICIP)

Self Cite

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“…were utilized to detect and locate animals in images, and then the animals were tracked based on their geometric features in continuous frames using different extra algorithms. These algorithms included popular trackers (e.g., simple online and real-time tracking, SORT; Hungarian algorithm; Munkres variant of the Hungarian assignment algorithm, MVHAA; spatial-aware temporal response filter, STRF; and channel and spatial reliability discriminative correlation filter tracker, CSRDCF) [ 61 , 83 , 88 , 96 , 103 ] and bounding box tracking based on Euclidean distance changes of centroids and probability of detected objects [ 71 , 95 , 118 , 125 , 230 ].…”

Section: Convolutional Neural Network Architecturesmentioning

confidence: 99%

Practices and Applications of Convolutional Neural Network-Based Computer Vision Systems in Animal Farming: A Review

Huang

Chen

et al. 2021

Sensors

102

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Convolutional neural network (CNN)-based computer vision systems have been increasingly applied in animal farming to improve animal management, but current knowledge, practices, limitations, and solutions of the applications remain to be expanded and explored. The objective of this study is to systematically review applications of CNN-based computer vision systems on animal farming in terms of the five deep learning computer vision tasks: image classification, object detection, semantic/instance segmentation, pose estimation, and tracking. Cattle, sheep/goats, pigs, and poultry were the major farm animal species of concern. In this research, preparations for system development, including camera settings, inclusion of variations for data recordings, choices of graphics processing units, image preprocessing, and data labeling were summarized. CNN architectures were reviewed based on the computer vision tasks in animal farming. Strategies of algorithm development included distribution of development data, data augmentation, hyperparameter tuning, and selection of evaluation metrics. Judgment of model performance and performance based on architectures were discussed. Besides practices in optimizing CNN-based computer vision systems, system applications were also organized based on year, country, animal species, and purposes. Finally, recommendations on future research were provided to develop and improve CNN-based computer vision systems for improved welfare, environment, engineering, genetics, and management of farm animals.

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