This paper presents a multi-view gait recognition algorithm for identification at a distance. We make use of two well known and effective gait representations namely Motion Silhouette Image (MSI) and gait energy image (GEI). MSI and GEI inherently capture the spatiotemporal characteristics of gait. We show that the individual recognition performance of MSI and GEI can be improved by using a fusion methodology. The features for MSI and GEI images are extracted using Independent Component Analysis (ICA) which is used widely in such applications. Extreme Learning Machine (ELM) classifier is then used for classification. ELM is a multiclass classifier which offers the advantage of less time consumption and high performance. The results are fused at score level making use of fusion rules such as min and max [17] to make the algorithm robust, reliable and to improve the performance of the system. Our approach is tested on the NLPR gait database. The NLPR gait database corresponds to 20 subjects, each subject has 4 sequences and there are 3 viewing angles (0deg, 45deg and 90deg) for each person. The results on the dataset show that the fusion gives good performance for the 3 views considered in this paper.

Ground subsidence occurrences have drastically increased in the Seoul area of the Republic of Korea. The structural defects of underground utilities were found to be the primary cause of ground subsidence based on several field investigations. This paper presents a risk model that assesses the probability of occurrence of ground subsidence along railways. In this study, support vector machine (SVM) and multi-layer perceptron (MLP) approaches were successfully employed to develop an artificial neural network (ANN)-based risk model. The risk model, in conjunction with a database composed of underground utilities and geological boring data along urban railway networks, was utilized to develop a hazard map system. A limited field experimental program was conducted for the purpose of verification, resulting in a promising tool to effectively maintain railway networks.

We propose a vision-based fall detection algorithm using advanced deep learning models and fusion methods for smart safety management systems. By detecting falls through visual cues, it is possible to leverage existing surveillance cameras, thus minimizing the need for extensive additional equipment. Consequently, we developed a cost-effective fall detection system. The proposed system consists of four modules: object detection, pose estimation, action recognition, and result fusion. Constructing the fall detection system involved the utilization of state-of-the-art (SOTA) models. In the fusion module, we experimented with various approaches, including voting, maximum, averaging, and probabilistic fusion. Notably, we observed a significant performance improvement with the use of probabilistic fusion. We employed the HAR-UP dataset to demonstrate this enhancement, achieving an average 0.84% increase in accuracy compared to the baseline, which did not incorporate fusion methods. By applying our proposed time-level ensemble and skeleton-based fall detection approach, coupled with the use of enhanced object detection and pose estimation modules, we substantially improved the robustness and accuracy of the system, particularly for fall detection in challenging scenarios.

Minkyu Cheon, and Heesung Lee. IJFIS 2017;17:58-67. https://doi.org/10.5391/IJFIS.2017.17.2.58

Gait recognition is a biometric technology that identifies individuals based on the unique characteristics of their gait. With the advancement of deep learning based computer vision technology, gait recognition has significantly improved in performance and gained significant attention due to its non-invasive nature. This paper proposes a vision-based gait recognition model that enhances performance by applying deformable convolution in the temporal dimension. Gait is a periodic behavior, and understanding the body’s changing patterns during walking is crucial for improving recognition accuracy. Important features for gait recognition can appear in both short and long moments of the walking process. Therefore, this study proposes a model that performs deformable convolution in the temporal dimension to effectively extract such features. This allows for a more flexible understanding of temporal patterns, eliminating the need for hand-crafted design of filters for extracting temporal features and enabling the model to learn important features for gait recognition that appear in both short and long time intervals in a unified manner. As a result, the proposed model achieved state-of-the-art performance on the CASIA-B dataset.

The objective of this work is to address the problem of detecting track intruders in railway systems using deep learning-based algorithms. Unauthorized entry onto railway tracks poses a significant risk of collisions between trains and humans. However, intrusion discrimination algorithms often suffer from a lack of learning data and data imbalance issues. To overcome these challenges, this research proposes an algorithm that combines generative models and classification networks. Generative models are utilized to generate synthetic intrusion data by learning the underlying distribution of available data and creating new samples resembling the original data. The augmented intrusion data is then used to train deep neural networks to accurately identify intrusions. The proposed algorithm is evaluated using real data sets, demonstrating its effectiveness in overcoming limited learning data and data imbalance issues. By augmenting intrusion data using generative models, the algorithm achieves improved accuracy compared to traditional approaches. In conclusion, the algorithm presented in this work provides a solution for detecting track intruders in railway systems. By leveraging generative models to augment limited intrusion data and utilizing classification networks for intrusion discrimination, the algorithm demonstrates improved performance in accurately identifying intrusions. This research highlights the potential of deep learning-based approaches in enhancing railway safety and recommends further exploration and application of these methods in real-world settings.