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Previously proposed human–animal-related techniques focus on the manual discrimination of single animal behaviors or simple human–animal interactions. To address the automatic detection and classification of complex animal behaviors and the animals reactions to human, we propose an approach built upon both the visual representation with Fisher vectors and the end-to-end generative hidden Markov model to facilitate the discrimination of both coarse- and fine-grained animal–human interactions. To satisfy the requirement for abundant data samples of the generative approach, we recorded and annotated more than 480 hours of videos featuring eight persons and 210 laying hens during the process of feeding and cleaning. The experimental results show that the proposed method outperforms state-of-the-art approaches. 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"Monitoring of pet animal in smart city is a big challenge for authorities concerned. The classical animal identification and monitoring methods fail to provide the required level of security and management of pet animals. Animal biometrics based recognition systems are considered a good alternative for the health management, tracking, identification, and security of pet animals. In this paper, we propose a low-cost system for monitoring of pet animals (dogs) based on their primary animal biometric identifiers. The proposed recognition approach uses the one-shot similarity and distance metric based learning methods for matching and classifying the extracted features of face images for recognition of pet animals (dog). We also developed a prototype for evaluating the accuracy of the recognition system. 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