This thesis investigates the development and use of software to measure respiratory frequency on cows using optronics and computer vision. It examines mainly two different strategies of image and signal processing and their performances for different input qualities. The effect of heat stress on dairy cows and the high transmission risk of pneumonia for calves make the investigation done during this thesis highly relevant since they both have the same symptom; increased respiratory frequency. The data set used in this thesis was of recorded dairy cows in different environments and from varying angles. Recordings, where the authors could determine a true breathing frequency by monitoring body movements, were accepted to the data set and used to test and develop the algorithms. One method developed in this thesis estimated the breathing rate in the frequency domain by Fast Fourier Transform and was named "N-point Fast Fourier Transform." The other method was called "Breathing Movement Zero-Crossing Counting." It estimated a signal in the time domain, whose fundamental frequency was determined by a zero-crossing algorithm as the breathing frequency. The result showed that both the developed algorithm successfully estimated a breathing frequency with a reasonable error margin for most of the data set. The zero-crossing algorithm showed the most consistent result with an error margin lower than 0.92 breaths per minute (BPM) for twelve of thirteen recordings. However, it is limited to recordings where the camera is placed above the cow. The N-point FFT algorithm estimated the breathing frequency with error margins between 0.44 and 5.20 BPM for the same recordings as the zero-crossing algorithm. This method is not limited to a specific camera angle but requires the cow to be relatively stationary to get accurate results. Therefore, it could be evaluated with the remaining three recordings of the data set. The error margins for these recordings were measured between 1.92 and 10.88 BPM. Both methods had execution time acceptable for implementation in real-time. It was, however, too incomplete a data set to determine any performance with recordings from different optronic devices.
| Identifer | oai:union.ndltd.org:UPSALLA1/oai:DiVA.org:liu-176354 |
| Date | January 2021 |
| Creators | Antonsson, Per, Johansson, Jesper |
| Publisher | Linköpings universitet, Datorseende, Linköpings universitet, Datorseende |
| Source Sets | DiVA Archive at Upsalla University |
| Language | English |
| Detected Language | English |
| Type | Student thesis, info:eu-repo/semantics/bachelorThesis, text |
| Format | application/pdf |
| Rights | info:eu-repo/semantics/openAccess |
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