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Detection and tracking of chickens in low-light images using YOLO network and Kalman filter

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Abstract

Continuous monitoring of chickens’ movement on-farm is a challenge. The present study aimed to associate the modified YOLO v4 model with a bird tracking algorithm based on a Kalman filter to identify a chicken’s movement using low-resolution video. The videos were captured in grayscale using a top-view camera with a low resolution of 702 × 480 pixels, preventing the application of usual image processing techniques. We used YOLO to extract the characteristics of the image and classification automatically. A dataset with images of tagged chickens was used to detect chickens, being 1000 frames tagged in different videos. The generated model was applied in a video that returned the bounding box of the location of the chicken in the frame. With the limits of the box, the centroid was calculated and exported in a CSV file for tracking processing. The Kalman filter was implemented to track chickens in low light intensity. Results indicated that YOLO presented a 99.9% accuracy in detecting chickens in low-quality videos. Using the Kalman filter, the algorithm tracks the chickens and gives them a particular identification number until they leave the compartment. Furthermore, each moving chicken is located in different colors along with the maps below the image, making chicken detection more convenient. The tracking results of chickens show that the proposed method can correctly handle the new entry and exit moving targets in crowded conditions.

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Acknowledgements

Funding was provided by National Council for Scientific and Technological Development - CNPq (Grant # 304085/2021-9 and # 308177/2021-7).

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Authors and Affiliations

  1. Graduate Program in Agribusiness and Development, School of Sciences and Engineering, São Paulo State University, Tupã, SP, Brazil

    Allan Lincoln Rodrigues Siriani

  2. Graduate Program in Agricultural Engineering, School of Agricultural Engineering, State University of Campinas, Campinas, SP, Brazil

    Vanessa Kodaira

  3. Department of Mechanics of Biosystems Engineering, Faculty of Agricultural Engineering, Agricultural Sciences and Natural Resources University of Khuzestan, Ahvaz, Iran

    Saman Abdanan Mehdizadeh

  4. Graduate Program in Production Engineering, Universidade Paulista, São Paulo, SP, Brazil

    Irenilza de Alencar Nääs

  5. School of Agricultural Engineering, Campinas State University, Campinas, SP, Brazil

    Daniella Jorge de Moura

  6. Department of Management, Development and Technology, School of Sciences and Engineering, São Paulo State University, Tupã, SP, Brazil

    Danilo Florentino Pereira

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  1. Allan Lincoln Rodrigues Siriani
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Correspondence to Danilo Florentino Pereira.

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Siriani, A.L.R., Kodaira, V., Mehdizadeh, S.A. et al. Detection and tracking of chickens in low-light images using YOLO network and Kalman filter. Neural Comput & Applic 34, 21987–21997 (2022). https://doi.org/10.1007/s00521-022-07664-w

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