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.

This study proposes a new method to evaluate the performance of algorithms for moving object detection (MODA) in video sequences. The proposed method is based on human performance metric intervals, instead of ideal metric values (0 or 1) which are commonly used in the literature. These intervals are proposed to establish a more reliable evaluation and comparison, and to identify areas of improvement in the evaluation of MODA. The contribution of the study includes the determination of human segmentation performance metric intervals and their comparison with state‐of‐the‐art MODA, and the evaluation of their segmentation results in a tracking task to establish the impact between performance and practical utility. Results show that human participants had difficulty with achieving a perfect segmentation score. Deep learning algorithms achieved performance above the human average, while other techniques achieved a performance between 88 and 92%. Furthermore, the authors demonstrate that algorithms not ranked at the top of the quantitative metrics worked satisfactorily in a tracking experiment; and therefore, should not be discarded for real applications.

This paper presents a new motion detection algorithm called Fuzzy Gray Difference Histogram (FGDH). The method consists of three main stages. The first stage obtains the gray level differences of a pixel centered in a LxL window with respect to its neighbors by the local binary similarity patterns (LBSP) operator. Then, these differences are fuzzified by a Gaussian function that carries out a weighting from 0 to 1, which indicates the degree of similarity of the neighborhood pixels with respect to the central one. The second stage obtains a background model based on fuzzy local histograms that are made from small neighborhoods in order to provide spatial and gray level information. The third stage updates the background histograms based on the similarity between the current and past histograms. Experimental results indicate that FGDH presents a competitive performance in the Change detection database considering the elimination of shadows and noise in video scenarios from the categories Baseline, Badweather and Shadow. The implementation was attained under an optimization scheme that reduced the computational cost and a parallelizable scheme of the method which allows real time speed.