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    A New Approach to Automatic Heat Detection of Cattle in Video
    (2019-01-01)
    Pasupa, Kitsuchart
    ;
    Lodkaew, Thanawat
    Heat detection of cattle in video is essential for dairy farm. A cow should be inseminated within a certain period of time in order for it to breed successfully. After it has given birth to a calf, it produces milk. This paper proposes the use of a set of discriminative features to detect cattle in heat, where the features were extracted from the behaviours of oestrus cow by a key-point analysis of locations of their body parts in a video. We evaluated our proposed features, in terms of the algorithm’s classification accuracy of identifying cow in heat, with several machine learning algorithms for two instances–using a global model and a number of cattle-specific models to execute the identification. It was found that Support Vector Machine with Radial Basis Function yielded a maximum accuracy of 90.0% for the global model and 92.0% for the cattle-specific models. These initial findings demonstrate that individual cows may have different oestrus behaviours, a fact that would benefit any dairy farmers. Our future development will be on a practical video monitoring and detection system of cows in heat in a dairy farm.
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    A comparative study of feature point matching versus foreground detection for computer detection of dairy cows in video frames
    (2015-12-01)
    Pasupa, Kitsuchart
    ;
    Pantuwong, Natapon
    ;
    Nopparit, Suthasinee
    Behaviours of dairy cows reflect their health and emotions. Behavioural analysis by video surveillance is an accepted technique for helping cow-keepers to spot their cows' health problems. To perform a behavioural analysis, the presence and location of the cows need to be detected first. In this study, we used feature point matching method and foreground detection method to detect them. Two experiments were conducted in a dairy farm to detect cows in video frames recorded by a video camera installed over the top of a free-stall barn. A total of 800 frames of recorded cows' activities were captured. True and false positive and negative results were statistically confirmed by t test. We found that the accuracies of the feature point matching and foreground detection methods were 38.55 and 75.95 %, respectively; hence, for our setup, the foreground detection was a better method.