## 1 Introduction
### Related Work  
In recent years, using remote still and video surveillance in the research of wildlife and management has grown rapidly [33; 34; 35]. Surveillance has many purposes, from the identification of pests or species with problem behaviors to the assessment of species diversity and distribution, and thus the importance of conservation. However, these usually share a common need, which is to identify a specific target species. In one study, they used image recognition as an estimation tool, preventing Mycobacterium bovis transmission between badgers and cattle. With growing interest in remote surveillance, capturing large amounts of image data becomes a challenge in identification. Before, screening pictures was required to be quality checked manually, which is expensive and time consuming [36; 37]. Thus, people are deeply interested in the emergence of automated methods [35]. Machine learning has an increasing trend in automatic animal identification, and it has also been applied in biological and fishery monitoring. These technologies have made great progress, and can shoot high-resolution images in challenging environments, and in the end can effectively manage natural resources [38]. However, the method of identifying animals depends on the situation. For instance, it has been successful when using colors to distinguish animals from the background in automatic detection and tracking of elephants. Furthermore, it also works on other species [35]. If there are a lot of visual data including target and non-target species that require manual review, machine learning that can automatically identify wildlife needs to be used. When it comes to classification, image features are required. Generally, the histogram of oriented gradient [39], one of hand-crafted image feature methods, has been widely applied. Scale-invariant feature transform (SIFT) is also another hand-crafted image feature method [40]. However, convolutional neural networks (CNN) [41; 42] perform better than all hand-crafted feature methods on large datasets [43]. For example, the ImageNet dataset includes 1.2 million images. In recent years, it has been applied to the automatic classification of wildlife, but its performance still has its limitations [37]. In Reference [44], the accuracy of deep learning classification photos was evaluated with the processing of a few collections of picture materials that are difficult to model. Their trained model was applied on a project from an online citizen science platform that provides researchers with access to millions of volunteers via www.zooniverse.org, where public volunteers can help ecologists classify large amounts of image data. Therefore, CNN was applied to differentiate among different species, humans, or vehicles and empty pictures (no animals, vehicles, or humans). Combining a trained model that has been classified by citizen scientists, manual work has been reduced by 43% while maintaining entire accuracy for a live experiment working on Zooniverse. It means that the trained neural network can correctly replace 43% of the human workload, and the result is the same as that done by humans. Besides, deep learning is used to automatically identify and separate species' specific activities from still images and video data using CNN. A total of 8368 wild and domestic animal images were used to develop a method to separate badgers from other species (two classifications), and then distinguish each from six species (multi-classification). This means making two classifications first to determine whether it is a target image, and if it is judged to be a target, then performing six classifications. In Reference [37], two deep learning frameworks are used to do automatic image recognition and get a high accuracy, 98.05%, for binary classification, and 90.32% for multi-classification. In 2017, CNN was used to train for recognizing and counting the behavior of 48 species in 3.2 million images [45].