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

Siriani, Allan Lincoln Rodrigues; Kodaira, Vanessa; Mehdizadeh, Saman Abdanan; Nääs, Irenilza de Alencar; Moura, Daniella Jorge de; Pereira, Danilo Florentino

doi:10.1007/s00521-022-07664-w

Cited by 25 publications

(27 citation statements)

References 43 publications

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“…However, the application of different AI techniques also has certain limitations (Figure 6). For example, computer vision-based methods like CNNs can accurately recognize animals and their behaviors, but may struggle in low light or cluttered environments [83,84]. Sensor-based approaches efficiently collect behavioral data, yet interpreting complex emotions remains difficult [85].…”

Section: Discussion and Future Prospectsmentioning

confidence: 99%

Advancements in artificial intelligence technology for improving animal welfare: Current applications and research progress

Zhang,

Guo,

et al. 2023

Animal Research and One Health

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The integration of Artificial Intelligence (AI) in various sectors has led to significant advancements, with the animal industry being no exception. This review aims to investigate the benefits, limitations, and future prospects of AI technology in improving animal welfare. First, it examines the role of AI in understanding animal behaviors and emotions, providing deeper insights into their well‐being and sources of stress. Next, the paper explores how AI can revolutionize animal nutrition through innovative algorithms and data analytics. The health aspect emphasizes the ability of AI to identify and manage illnesses through intelligent systems. This review also highlights the application of AI in improving animal living conditions, with a focus on environmental management and automated cleaning and disinfection systems. In conclusion, the review emphasizes AI‐driven techniques for early prediction, close monitoring, and accurate diagnosis of animal diseases, ensuring healthier and more sustainable livestock management. By leveraging its advantages, addressing limitations, and exploring future directions, AI has the potential to significantly enhance animal welfare, sustainable agriculture, and veterinary practices.

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Section: Discussion and Future Prospectsmentioning

confidence: 99%

Advancements in artificial intelligence technology for improving animal welfare: Current applications and research progress

Zhang,

Guo,

et al. 2023

Animal Research and One Health

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“…To solve this challenge, a Kalman filter was used to compute the distance between each centroid and the old one, in order to check if the chicken had been tracked before. [7]. The proposed model used YOLOv4 in order to detect chickens from input frames in addition to returning the bounding box positions to store in CSV files for the tracking process.…”

Section: Related Workmentioning

confidence: 99%

“…As illustrated in Table I, Neethira- jan [6] put forward a system for detecting and counting chickens using Yolov5 and Kalman Filter. Siriani [7] also employed Yolov4 and Kalman filter to monitor chickens' movements in low light conditions. Meanwhile, Fang et al proposed a system that classifies chicken behavior by analyzing their poses using DeepLabCut [12] and ResNet50 [13].…”

Section: Introductionmentioning

confidence: 99%

Chicken Behavior Analysis for Surveillance in Poultry Farms

Mohialdin¹,

Elbarrany²,

Atia³

2023

IJACSA

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Poultry farming is an important industry that provides food for a growing population. However, the welfare of the birds is a major concern, as poor living conditions leads to abnormal behavior that affects the health and productivity of the flock. In order to monitor and improve the welfare of the birds, it is important to have a surveillance system in place that monitors the behavior of the chickens and alert farmers to potential issues. This paper reviews the current state of the art in behavior analysis for surveillance in poultry farms and discuss potential future directions for research in this area. This paper presents a computer-vision-based system that detects and monitors the behaviors of the chickens in poultry farms. The system classifies three behaviors which are eating, walking and sleeping. The system takes videos as input and then classifies the behavior of the chicken. The proposed system produces an accuracy of 94.7% using Light Gradient Boosting Machine on a collected data-set of chickens, and a 98.4% accuracy on a benchmarked Human Activity Recognition data-set.

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“…In poultry farming, it is essential to monitor and understand bird movement and behavior closely. This not only ensures the well-being of the animals but also helps improve production efficiency in a sustainable environment [ 2 , 3 , 4 ]. Chickens display a variety of behaviors, including different movement patterns, social interactions, and reactions to their surroundings.…”

Section: Introductionmentioning

confidence: 99%

Deep Learning Methods for Tracking the Locomotion of Individual Chickens

Yang,

Bist,

Paneru

et al. 2024

Animals

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Poultry locomotion is an important indicator of animal health, welfare, and productivity. Traditional methodologies such as manual observation or the use of wearable devices encounter significant challenges, including potential stress induction and behavioral alteration in animals. This research introduced an innovative approach that employs an enhanced track anything model (TAM) to track chickens in various experimental settings for locomotion analysis. Utilizing a dataset comprising both dyed and undyed broilers and layers, the TAM model was adapted and rigorously evaluated for its capability in non-intrusively tracking and analyzing poultry movement by intersection over union (mIoU) and the root mean square error (RMSE). The findings underscore TAM’s superior segmentation and tracking capabilities, particularly its exemplary performance against other state-of-the-art models, such as YOLO (you only look once) models of YOLOv5 and YOLOv8, and its high mIoU values (93.12%) across diverse chicken categories. Moreover, the model demonstrated notable accuracy in speed detection, as evidenced by an RMSE value of 0.02 m/s, offering a technologically advanced, consistent, and non-intrusive method for tracking and estimating the locomotion speed of chickens. This research not only substantiates TAM as a potent tool for detailed poultry behavior analysis and monitoring but also illuminates its potential applicability in broader livestock monitoring scenarios, thereby contributing to the enhancement of animal welfare and management in poultry farming through automated, non-intrusive monitoring and analysis.

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

Cited by 25 publications

References 43 publications

Advancements in artificial intelligence technology for improving animal welfare: Current applications and research progress

Advancements in artificial intelligence technology for improving animal welfare: Current applications and research progress

Chicken Behavior Analysis for Surveillance in Poultry Farms

Deep Learning Methods for Tracking the Locomotion of Individual Chickens

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