How to navigate safely, recognize encountered obstacles, and move independently from one location to another in unknown environments are some of the challenges that face visually impaired people. By proposing a solution towards overcoming these challenges, this work will be of most importance to visually impaired people. In this work, we propose a consistent, reliable and robust smartphone-based method to classify obstacles in unknown environments from partial visual information based on computer vision and machine learning techniques. Our proposed method handles high levels of noise and bad resolution in frames captured from a phone camera. In addition, our proposed method offers maximum flexibility to users and use the least expensive equipment possible. Moreover, by leveraging on deep-learning techniques, the proposed method enables semantic categorization in order to classify obstacles and increase the awareness of the explored environment. The efficiency of the work has been experimentally measured on a variety of experiments studies on different complex scenes. It records high accuracy of [90.2 % ].

Traffic flow prediction is a pivotal element in Intelligent Transportation Systems (ITSs) that provides significant opportunities for real-world applications. Capturing complex and dynamic spatio-temporal patterns within traffic data remains a significant challenge for traffic flow prediction. Different approaches to effectively modeling complex spatio-temporal correlations within traffic data have been proposed. These approaches often rely on a single model to capture temporal dependencies, which neglects the varying influences of different time periods on traffic flow. Additionally, these models frequently utilize either static or dynamic graphs to represent spatial dependencies, which limits their ability to address complex and overlapping spatial relationships. Moreover, some approaches struggle to fully capture spatio-temporal variations, leading to the exclusion of critical information and ultimately resulting in suboptimal prediction performance. Thus, this paper introduces the Adaptive Spatio-Temporal Attention-Based Multi-Model (ASTAM), an architecture designed to capture spatio-temporal dependencies within traffic data. The ASTAM employs multi-temporal gated convolution with multi-scale temporal input segments to model complex non-linear temporal correlations. It utilizes static and dynamic parallel multi-graphs to facilitate the modeling of complex spatial dependencies. Furthermore, this model incorporates a spatio-temporal self-attention mechanism to adaptively capture the dynamic and long-term spatio-temporal variations in traffic flow. Experiments conducted on four real-world datasets reveal that the proposed architecture outperformed 13 baseline approaches, achieving average reductions of 5.0% in MAE, 13.28% in RMSE, and 6.46% in MAPE across four datasets.

No one refutes the importance of datasets in the development of any new approach. Despite their importance, open access datasets in computer vision remain insufficient for some applications. This paper introduces a FallFree, new and rich dataset that can be used for the evaluation/development of computer vision-based applications pertinent to people who use a cane as a mobility aid, e.g., fall detection, activity recognition. In particular, the FallFree dataset includes video streams captured with Kinect which offers a wide range of visual information. It is organized hierarchically, in terms of scenarios each of which is structured in terms of its features. The current FallFree dataset version covers all fall scenarios of the cane users along with various non-fall scenarios grouped into one set. Each scenario is represented through a rich set of features that can be extracted from Kinect. To widen its usability, the dataset was constructed while accounting for existing datasets' organization, size, scope, streams, types and hypotheses.