Fast Region-Adaptive Defogging and Enhancement for Outdoor Images Containing Sky

Creating intelligent systems capable of recognizing emotions is a difficult task, especially when looking at emotions in animals. This paper describes the process of designing a “proof of concept” system to recognize emotions in horses. This system is formed by two elements, a detector and a model. The detector is a fast region-based convolutional neural network that detects horses in an image. The model is a convolutional neural network that predicts the emotions of those horses. These two elements were trained with multiple images of horses until they achieved high accuracy in their tasks. In total, 400 images of horses were collected and labeled to train both the detector and the model while 40 were used to test the system. Once the two components were validated, they were combined into a testable system that would detect equine emotions based on established behavioral ethograms indicating emotional affect through the head, neck, ear, muzzle, and eye position. The system showed an accuracy of 80% on the validation set and 65% on the test set, demonstrating that it is possible to predict emotions in animals using autonomous intelligent systems. Such a system has multiple applications including further studies in the growing field of animal emotions as well as in the veterinary field to determine the physical welfare of horses or other livestock.

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Animal Detection in Highly Cluttered Natural Scenes by using Faster R-CNN

International Journal of Recent Technology and Engineering - 2 ◽

10.35940/ijrte.b1059.0882s819 ◽

2019 ◽

Vol 8 (2S8) ◽

pp. 1311-1313

Keyword(s):

Computer Vision ◽

Environmental Protection ◽

Detection Methods ◽

Detection Accuracy ◽

Natural Scenes ◽

Human Beings ◽

Intelligent Monitoring ◽

Animal Detection ◽

Fast Region ◽

Important Branch

With the increasing awareness of environmental protection, people are paying more and more attention to the protection of wild animals. Their survive-al is closely related to human beings. As progress in target detection has achieved unprecedented success in computer vision, we can more easily tar-get animals. Animal detection based on computer vision is an important branch of object recognition, which is applied to intelligent monitoring, smart driving, and environmental protection. At present, many animal detection methods have been proposed. However, animal detection is still a challenge due to the complexity of the background, the diversity of animal pos-es, and the obstruction of objects. An accurate algorithm is needed. In this paper, the fast Region-based Convolutional Neural Network (Faster R-CNN) is used. The proposed method was tested using the CAMERA_TRAP DATASET. The results show that the proposed animal detection method based on Faster R-CNN performs better in terms of detection accuracy when its performance is compared to conventional schemes

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