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Intelligent surveillance systems in multi‐camera environments pose a hard‐open problem for computer vision. The way the people look changes inside and also among cameras, so people re‐identification task can be largely improved collecting data about people already identified and take advantage of it as time advances in surveillance video. Furthermo...
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The major approaches of transfer learning in computer vision have tried to adapt the source domain to the target domain one-to-one. However, this scenario is difficult to apply to real applications such as video surveillance systems. As those systems have many cameras installed at each location regarded as source domains, it is difficult to identif...
Citations
... The technology of person re-identification finds extensive applications in abnormal behaviour detection, intelligent security, crowd counting, and various other scenes. Despite notable progress in recent years [2][3][4][5], the domain of person re-identification continues to face challenges such as low image resolution, diverse shooting angles, imbalanced lighting conditions, pose variations, and occlusions, which leave substantial room for improvement in its performance. ...
Person re‐identification represents a pivotal sub‐problem in image retrieval, boasting broad application prospects in fields such as intelligent security and video surveillance. However, most existing person re‐identification methods predominantly focus solely on visual features pertaining to the person targets, thereby disregarding some supporting information closely related to the scene context. In the context of athlete re‐identification during sports event scenes, the athlete bib number is fully considered, an important clue that can provide different athletes' identities, and the traditional visual features of the person and high‐level semantic information of the bib number text are fused. A multi‐source information mutual gain mechanism is designed to improve the accuracy of the person re‐identification task. In the existing only publicly available marathon bib number dataset RBNR, the recognition accuracy of this method is significantly superior to that of the existing person re‐identification method. In addition, this paper constructs and publishes an athlete re‐identification dataset (HNNU‐ReID8000) for mainstream sports events, and the mean average precision (mAP) value of this method reaches 96.1% on this dataset, significantly ahead of existing state‐of‐the‐art person re‐identification methods. The code and the HNNU‐ReID8000 dataset will be released at https://github.com/yanbin‐zhu/zyb_person‐reid.
... Using SSS datasets for pedestrian gender classification, another such dataset named as PKU-Reid is chosen. The PKU-Reid dataset is preferably used for pedestrian reidentification [36][37][38]. This dataset consists of 114 individuals (70 males and 44 females), where the appearances of each individual are captured in eight directions and resultantly 1824 images are collected from two non-overlapping cameras. ...
In this manuscript, imbalanced and small sample space (IB-SSS) dataset problems for pedestrian gender classification using fusion of selected deep and traditional features (PGC-FSDTF) are considered. In this regard, data preparation is first done through data augmentation and preprocessing steps to handle imbalanced classification problem and environmental effects, respectively. The proposed approach follows different types of feature extraction schemes, for instance, pyramid histogram of oriented gradients, hue saturation value histogram, deep visual features of DenseNet201 and InceptionResNetV2-based convolutional neural network architectures. The parallel fusion method computes the maximum and average values-based features from the learned features of both deep networks. Features are selected through features selection methods such as entropy and principal component analysis (PCA). The subsets of features are serially fused and provided to multiple classifiers to perform gender classification on IB-SSS datasets. Resultantly, the proposed PGC-FSDTF method shows better results in terms of different accuracies (overall, mean, and balanced), and area under curve on selected datasets. Further, improved results are achieved on applied datasets using PCA-based selected features and medium Gaussian support vector machine (M-SVM) classifier. These results on different datasets confirm that the selected feature combination provides a way to handle IB-SSS issues for PGC effectively.
... Person re-identification (ReID) is to retrieve person images belonging to the same identification [1,2], which is of great significance in practical applications. It is widely known as a challenging task due to the complicated environment or changeable human appearance on different occasions or at different times [3]. In recent years, more and more researchers have been dedicated to this task, and great progress has been made. ...
An occluded person re‐identification (ReID) approach is presented by constructing a Bi‐level deep Mutual learning assisted Multi‐task network (BMM), where the holistic and occluded person ReID tasks are treated as two related but not identical tasks. This is inspired by the human perception characteristic that there exist both similarities and differences when human views a holistic image and the occluded one. Specifically, a multi‐task network with two branches is designed, where the convolutional neural network based feature representation part shares the weights by two tasks for commonality extraction, while the following output layers have respective weights for difference representation. Furthermore, as the non‐occluded regions convey discriminative information, a bi‐level mutual learning strategy is proposed and applied mutually on two branches to obtain more effective information from the non‐occluded regions in the occluded images for better identity recognition. This is achieved by both feature‐level and output‐level mutual loss functions. Extensive experiments prove the advantages of the BMM for person ReID.
... Surveillance systems analyze images in real time faster than a human. Due to this situation it is necessary to obtain help from technology that allows the processing of these images [3]. A commonly used tool is computer vision (CV) which currently has a wide application in different areas such as: video surveillance, traffic control, human-machine interaction [4]. ...
... Image processing using computer tools such as VC is not an easy task, because there are several factors that affect image quality such as: occlusion, low quality, lighting, shadows among others [1]. Therefore, through the use of algorithms, techniques and methods used in computer vision, important results have been obtained when analyzing objects captured by cameras in controlled environments [3]. OpenCV must be used for facial recognition because it is a well-known, precise and fast tool for any type of image. ...
In order to solve the requirements of scheduling automation system for access control and the defects of access control model, an automatic access control model of power information system based on intelligent semantic network was designed. The overall structure of access control model of power information system is given. The detector is designed by artificial intelligence technology, combining artificial neural network and artificial immune algorithm, which provides the basis for checking access request module. By checking the access request module to find out whether there is a detector matching the access request, to judge whether the access request is legitimate. The verification results show that, the model can effectively support the access and modification of legitimate users and prevent the access of illegal users with high control precision.
