International Association for Development of the Information Society, 2017
People re-identification has been a very active research topic recently in computer vision. It is... more People re-identification has been a very active research topic recently in computer vision. It is an important application in surveillance system with disjoint cameras. This paper is focused on the implementation of a human re-identification system. First the face of detected people is divided into three parts and some soft-biometric traits are extracted from each part. In second step, we can recognize people even if their faces are hidden or they are with back appearance. The features extraction will be carried out according to the overall characteristics of the complete images of different persons. An algorithm that identifies people from their body shape will be developed. A powerful representation of the person based on the characteristics of color, texture and shape as well as different soft-biometric features is suggested. The experiments are carried out on SAIVT-SoftBio database which consists of videos from disjoint surveillance cameras as well as some static image based dat...
Recently, Deep Neural Networks (DNNs) have become a central subject of discussion in computer vis... more Recently, Deep Neural Networks (DNNs) have become a central subject of discussion in computer vision for a broad range of applications, including image classification and face recognition. Compared to existing conventional machine learning methods, deep learning algorithms have shown prominent performance with high accuracy and speed. However, they always require a large amount of data to achieve adequate robustness. Furthermore, additional samples are time-consuming and expensive to collect. In this paper, we propose an approach that combines generative methods and basic manipulations for image data augmentations and the FaceNet model with Support Vector Machine (SVM) for face recognition. To do so, the images were first preprocessed by a Deep Convolutional Generative Adversarial Net (DCGAN) to generate samples having realistic properties inseparable from those of the original datasets. Second, basic manipulations were applied on the images produced by DCGAN in order to increase th...
In this study, the authors present a new approach to segment and classify moving objects in video... more In this study, the authors present a new approach to segment and classify moving objects in video sequences by combining an unsupervised anomaly discovery framework called DeepSphere and generative adversarial networks. The proposed deep detector classifier employs and validates DeepSphere, which aims mainly to identify the anomalous cases in the spatial and temporal context in order to perform foreground objects segmentation. For post-processing, some morphological operations are considered to better segment and extract the desired objects. Finally, they take advantage of the power of generative models, which recognise the problem of semi-supervised learning as a specific missing data imputation task in order to classify the segmented objects. They evaluate the method with multiple datasets and the results confirm the effectiveness of the proposed approach, which achieves superior performance over the state-of-the-art methods having the capabilities of segmenting and classifying moving objects from videos surveillance.
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