Birds of prey especially eagles and hawks have a visual acuity two to five times better than humans. Among the peculiar characteristics of their biological vision are that they have two types of foveae; one shallow fovea used in their binocular vision, and a deep fovea for monocular vision. The deep fovea allows these birds to see objects at long distances and to identify them as possible prey. Inspired by the biological functioning of the deep fovea a model called DeepFoveaNet is proposed in this paper. DeepFoveaNet is a convolutional neural network model to detect moving objects in video sequences. DeepFoveaNet emulates the monocular vision of birds of prey through two Encoder-Decoder convolutional neural network modules. This model combines the capacity of magnification of the deep fovea and the context information of the peripheral vision. Unlike algorithms to detect moving objects, ranked in the first places of the Change Detection database ( CDnet14 ), DeepFoveaNet does not depend on previously trained neural networks, neither on a huge number of training images for its training. Besides, its architecture allows it to learn spatiotemporal information of the video. DeepFoveaNet was evaluated in the CDnet14 database achieving high performance and was ranked as one of the ten best algorithms. The characteristics and results of DeepFoveaNet demonstrated that the model is comparable to the state-of-the-art algorithms to detect moving objects, and it can detect very small moving objects through its deep fovea model that other algorithms cannot detect.

中文翻译：

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DeepFoveaNet：用于检测移动物体的 Deep Fovea Eagle-Eye Bioinspired 模型

猛禽，尤其是鹰和鹰的视力是人类的两到五倍。他们的生物视觉的特殊特征之一是他们有两种类型的中央凹；一个用于双眼视觉的浅中央凹和一个用于单眼视觉的深中央凹。深中央凹使这些鸟类能够看到远距离的物体并将它们识别为可能的猎物。受到深凹的生物功能的启发，一种称为DeepFoveaNet 是本文提出的。 DeepFoveaNet 是一种卷积神经网络模型，用于检测视频序列中的运动物体。 DeepFoveaNet 通过两个 Encoder-Decoder 卷积神经网络模块模拟猛禽的单眼视觉。该模型结合了深凹的放大能力和周边视觉的上下文信息。与检测移动物体的算法不同，在 Change Detection 数据库中排名第一（ CDnet14), DeepFoveaNet 不依赖于先前训练过的神经网络，也不依赖于大量训练图像进行训练。此外，它的架构允许它学习视频的时空信息。DeepFoveaNet 在 CDnet14 数据库实现高性能，被评为十大最佳算法之一。特点和结果DeepFoveaNet 证明该模型在检测运动物体方面可与最先进的算法相媲美，并且它可以通过其深凹模型检测到其他算法无法检测到的非常小的运动物体。