# Deep Learning Resolves Representative Movement Patterns in a Marine Predator Species
## 2 Related Work  
Deep learning is a subfield of machine learning. Based on artificial neural networks, deep learning is able to learn representations of data with multiple levels of abstraction and has significantly improved the state-of-the-art in many areas [9]. For example, in hyperspectral data classification, a deep stacked autoencoder model can obtain useful high-level features and provide competitive performance [10]. In image classification and object recognition, deep convolutional neural network based approaches perform far better than other approaches [11; 12]. In time series analysis, deep learning has been used to classify sleep stages with polysomnography signals [13]. Yet, for marine animals, deep learning mostly focuses on animal detection in images [14].  
Figure 1: The recurrent neural network with confidence measure (RNN-CM) process with sample inputs. Left side contains the input segments from marine animals with multiple age or gender groups, which are processed by RNN-CM. The processed segments can be divided into high and low confidence ones. The patterns of low confidence segments are shared by different animal groups, and thus, RNN-CM has low confidence in identifying which group they are extracted from. High confidence segments can be further divided according to animal age or gender groups, with relatively high confidence.  
For describing telemetry data, many models previously developed have been improved by cross-disciplinary efforts to quantify, interpret and ultimately understand movement patterns [3; 15]. It is likely that real-world movement paths of animals, here called \"trajectories\", contain statistically detectable, and ecologically significant information [16]. [PERSON] et al. described the three different basic models of home-range movement, memory-based movement and Levy movement [17]. State-space models (SSMs) have been widely used on animal telemetry data to identify different behavioural states [18], such as migrating and foraging. To increase the flexibility of the state-space model, Markov Chain Monte Carlo methods are introduced to model multiple states, and a kind of hidden Markov model (HMM) can identify \"exploratory\" or \"encamped\" states for movement trajectories at different time steps [19]. Here, trajectory segments in exploratory state contain many long steps and few turnings, and those in encamped state contain short steps and more frequent reversals. Neural networks have also been used to estimate the probability density of an animal's next location based on knowledge of distance, resources, and memory [20].  
Many algorithms have been developed for different scenarios. Some researchers have studied the relationship patterns including attraction, avoidance, and following between capuchin monkeys [21]. Some work has focused on the trajectory data clustering [22] and aggregation [23]. Trajectory classification usually focuses on human beings to identify different transportation modes, such as walking and driving [24]. For the marine environment, although many previous models focused on modeling trajectories using multiple states, such as exploratory or migratory states with few turns and encamped or resident states with frequent reversals, the trajectories of different groups of animals in the same species have not been the focus of much research.  
To probe this problem, we propose a new technique for capturing representative patterns in trajectories among subgroups of marine animals. Unlike other models (e.g., SSM [18], HMM [19]) focusing on different behavioral metrics (e.g., hunting time), the RNN-CM model is able to identify small scale movement patterns for specific animal groups. Our approach is an important step toward a more integrated assessment of animals' activities that are difficult to observe, which can assist in answering broader questions to more specifically address what marine animals are doing at various stages in their migration.