• Abstract

    Analysis of animal behavior requires proper algorithms for the extraction of desired information from videos. Animal behavior involves various states like facial expression, body movement etc. With the advancement in hardware, deep learning has become popular for analyzing the complex and large dataset. Deep learning algorithms have proved their significance on the benchmark dataset. In this paper, we used Residual Nets for classifying three-hour video containing egg laying induced activity changes in Drosophila. We obtained 99.5% accuracy and found significant improvement in accuracy as compared to CNN (Convolutional Neural Networks). Further, it is suggested that this technique can be used for analysis of animal behavior as well as activities of other domain like object detection, speech recognition, and character recognition, among others.

  • References

    1. Deng L, Dong Y (2014) Deep learning: methods and applications. Foundations and Trends® in Signal Processing 7: 197-387.
    2. Szegedy C, Ioffe S, Vanhoucke V, Alemi A (2017) Inception-v4, inception-resnet and the impact of residual connections on learning. AAAI 4:15-30.
    3. Sünderhauf N, Brock O, Scheirer W, Hadsell R, Fox D, Leitner J, Upcroft B, Abbeel P, Burgard W, Milford M, Corke P (2018) The limits and potentials of deep learning for robotics.The International Journal of Robotics Research 37:405-420.
    4. Patricia P, Jaen J, Catala A (2017) Assessing machine learning classifiers for the detection of animals’ behavior using depth-based tracking. Expert Systems with Applications 86:235-246.
    5. Dalziel, Benjamin D, Morales JM, Fryxell JM (2008) Fitting probability distributions to animal movement trajectories: using artificial neural networks to link distance, resources, and memory. The American Naturalist 2:248-258.
    6. Nadimi, Shahrak E, Søgaard HT, Bak T (2008) ZigBee-based wireless sensor networks for classifying the behaviour of a herd of animals using classification trees. Biosystems engineering 100:167-176.
    7. Hokkanen, Helena A (2011) Predicting sleep and lying time of calves with a support vector machine classifier using accelerometer data. Applied Animal Behaviour Science 134:10-15.
    8. Jinkui C (2012) A comparative study in birds: call-type-independent species and individual recognition using four machine-learning methods and two acoustic features. Bioacoustics 21:157-171.
    9. Bidder, Owen R (2014) Love thy neighbour: automatic animal behavioural classification of acceleration data using the k-nearest neighbour algorithm. PloS one 10:15-25.
    10. Ritaban D (2015) Dynamic cattle behavioural classification using supervised ensemble classifiers. Computers and Electronics in Agriculture 111:18-28.
    11. Mikael N (2015) Development of automatic surveillance of animal behaviour and welfare using image analysis and machine learned segmentation technique. Animal 11:1859-1865.
    12. MdSumon S (2015) Heat event detection in dairy cows with collar sensors: An unsupervised machine learning approach. Sensors 10:15-21.
    13. Daniel S (2015) Bag of class posteriors, a new multivariate time series classifier applied to animal behaviour identification. Expert Systems with Applications 42:3774-3784.
    14. Ulrich S, He R, Yang CH (2015) Analyzing animal behavior via classifying each video frame using convolutional neural networks. Scientific reports 5:14351.
    15. Ladds, Monique A (2016) Seeing it all: evaluating supervised machine learning methods for the classification of diverse otariidbehaviours. PloS one 11:18-28.
    16. Barajas L, Vianey (2017) "Analysis of animal accelerometer data using hidden Markov models. Methods in Ecology and Evolution 8:161-173.
    17. Ashfaqur R (2016) A comparison of autoencoder and statistical features for cattle behaviour classification. Neural Networks (IJCNN) International Joint Conference on IEEE.
    18. Norouzzadeh MS, Nguyen A, Kosmala M, Swanson A, Packer C, Clune J (2018) Automatically identifying, counting, and describing wild animals in camera-trap images with deep learning. PNAS 115:E5716-E5725.
    19. Villa AG, Salazar A, Vargas F (2017) Towards automatic wild animal monitoring: identification of animal species in camera-trap images using very deep convolutional neural networks. Ecological Informatics 41:24-32.
    20. Valletta, JJ (2017) Applications of machine learning in animal behaviour studies. Animal Behaviour 124:203-220.
    21. Morota G (2018) Big data analytics and precision animal agriculture symposium: Machine learning and data mining advance predictive big data analysis in precision animal agriculture. Journal of Animal Science 96:1540-1550.

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How to cite

Yadav, S., & Bist, A. S. (2019). Residual nets for understanding animal behavior. Journal of Animal Behaviour and Biometeorology, 7(2), 97–103. https://doi.org/10.31893/2318-1265jabb.v7n2p97-103
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