A Context-Aware Artificial Intelligence-based System to Support Street Crossings For Pedestrians with Visual Impairments

Full Terms & Conditions of access and use can be found at https://www.tandfonline.com/action/journalInformation?journalCode=uaai20 Applied Artificial Intelligence An International Journal ISSN: (Print) (Online) Journal homepage: https://www.tandfonline.com/loi/uaai20 A Context-Aware Artificial Intelligence-based System to Support Street Crossings For Pedestrians with Visual Impairments Aleksandro Montanha, Andreea M. Oprescu & MCarmen Romero-Ternero To cite this article: Aleksandro Montanha, Andreea M. Oprescu & MCarmen Romero-Ternero (2022) A Context-Aware Artificial Intelligence-based System to Support Street Crossings For Pedestrians with Visual Impairments, Applied Artificial Intelligence, 36:1, 2062818, DOI: 10.1080/08839514.2022.2062818 To link to this article: https://doi.org/10.1080/08839514.2022.2062818 © 2022 The Author(s). Published with license by Taylor & Francis Group, LLC. Published online: 12 Apr 2022. Submit your article to this journal Article views: 1006 View related articles View Crossmark data A Context-Aware Artificial Intelligence-based System to Support Street Crossings For Pedestrians with Visual Impairments Aleksandro Montanha a , Andreea M. Oprescu b , and MCarmen Romero-Ternero b a Programa Doctorado Ingeniería Informática, Escuela Técnica Superior de Ingeniería Informática, Universidad de Sevilla, Seville, Spain; b Departamento de Tecnología Electrónica, Universidad de Sevilla, Seville, Spain ABSTRACT Artificial intelligence has the potential to support and improve the quality of life of people with disabilities. Mobility is a potentially dangerous activity for people with impaired ability. This article presents an assistive technology solution to assist visually impaired pedestrians in safely crossing the street. We use a signal trilateration technique and deep learning (DL) for image processing to segment visually impaired pedestrians from the rest of pedestrians. The system receives information about the presence of a potential user through WiFi signals from a mobile application installed on the user’s phone. The software runs on an intelligent semaphore originally designed and installed to improve urban mobility in a smart city context. This solution can communicate with users, interpret the traffic situation, and make the necessary adjustments (with the semaphore’s capabilities) to ensure a safe street crossing. The proposed system has been implemented in Maringá, Brazil, for a one-year period. Trial tests carried out with visually impaired pedestrians confirm its feasibility and practicality in a real-life environment. ARTICLE HISTORY Received 7 February 2022 Revised 30 March 2022 Accepted 1 April 2022 Introduction Information and communication technology (ICT) interventions have an undeniable potential to help people with disabilities live their lives more independently and improve their quality of life (Lancioni et al. 2020). Artificial intelligence (AI) and mobile technologies have been shown to support people with disabilities by increasing their participation in society, helping them communicate, learn, shop, travel, move around town, among many others (Baumgatner, Rohrbach, and Schönhagen 2021), (Molina-Cantero et al. 2019), (Balasuriya et al. 2017), (Mahmud et al. 2020). Specifically, assistive technologies using AI can improve the quality of life of people with visual disabilities. According to the World Health Organization, 2.2 billion people live with near or far-reaching vision impairment throughout CONTACT Aleksandro Montanha [email protected] Programa Doctorado Ingeniería Informática, Escuela Técnica Superior de Ingeniería Informática, Universidad de Sevilla, Avda. Reina Mercedes S/n, 41012 Seville, Spain APPLIED ARTIFICIAL INTELLIGENCE 2022, VOL. 36, NO. 1, e2062818 (2821 pages) https://doi.org/10.1080/08839514.2022.2062818 © 2022 The Author(s). Published with license by Taylor & Francis Group, LLC. This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial License (http:// creativecommons.org/licenses/by-nc/4.0/), which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. the world (World Health Organization 2021). A challenging everyday activity for people with blindness or visual disabilities is mobility and, more specifically, street crossing (Hakobyan et al. 2013). Recently, more cities have already implemented accessibility features for street crossings, mainly in the form of sound alerts emitted by traffic lights. This alert can guide pedestrians by emitting a sound when the traffic light is green. However, in cities where noise pollution is an issue, this type of solution may not be sufficient to provide the accessibility that people with visual disabilities need. In addition, cases where intersections are close to each other can also create confusion for visually impaired pedestrians. If there are multiple traffic lights that emit sound alerts at a single intersection, it can be confusing and time consuming for visually impaired pedestrians to recognize when it is safe to cross. Therefore, the sound alert system presents many deficiencies in terms of its ability to transcribe the environment. With solutions that can transcribe the environment and act on the traffic situation if necessary, besides potentially providing a safer option, could be empowering and improve the interaction of visually impaired users with the urban environment. Currently, with the advent of the Internet of Things (IoT), which combines ubiquitous computing, sensing technologies, Internet communication protocols, and embedded devices (Suzuki 2017), a scenario with numerous solutions for smart city environments has emerged. Smartphones, as an important part of this paradigm, are widely available today to the general public. More importantly, in this context, visually impaired people use smartphones extensively as assistive technology (Khan & Khusro, 2020), (Retorta and Cristovão 2017). We introduce a context-sensitive system that guides visually impaired people at street crossings by automatically detecting pedestrians and vehicles in traffic. The hardware installation at an intersection consists of smart traffic lights (Seebot Agent) 1 paired with cameras and WiFi antennas. Communication between the system and the visually impaired pedestrian is achieved through a mobile application installed on the user’s smartphone. This method uses a signal trilateration technique and a computer vision pre-trained model to compute the user’s location and the traffic situation. Throughout the design and development process of this solution, potential users with visual impairment participated. Furthermore, field tests have been carried out with visually impaired users to test the application, which yielded positive results. The innovation of this work lies in 1) the solution has been built on existing hardware, an intelligent semaphore that was initially installed in Brazilian cities for smart city solutions related to traffic management; 2) the ability to transcribe the traffic situation to the user and to act on the situation to ensure user safety, since the semaphore can alter its behavior when a visually impaired pedestrian is crossing, if needed. APPLIED ARTIFICIAL INTELLIGENCE e2062818-2805 Related Work Mobility solutions for visually impaired users have been widely addressed in the literature. Advances in pedestrian detection and tracking using computer vision and machine learning techniques are discussed in (Brunetti et al. 2018). The authors call for more architectures to be implemented and tested. Additionally, one solution they point toward includes the use of RGB cameras paired with a DL strategy. (Chinchole and Patel 2017) developed a stick solution using an Arduino Nano microcontroller, two ultrasonic sensors, and an accelerometer. The authors also used a smartphone to obtain images of the user’s surroundings. After processing with artificial intelligence techniques, these images provided information about the user’s environment. A path hole detection system has been developed by (Islam and Sadi 2018) to assist visually impaired pedestrians using convolution neural networks (CNN). The authors achieved positive accuracy results in the experiments performed. A stick prototype for staircase and ground detection using a camera and an ultrasonic sensor has been proposed by (Habib et al. 2019). They used a pre-trained CNN model for training the system. In addition, there is no doubt that crossing the streets is a major challenge for visually impaired pedestrians. In this sense, an important number of solutions have been proposed in the literature. Some efforts have been devoted to developing an intelligent solution that includes traffic lights (through a connected device) in the cloud (Amazon Web Services-based system) and then alerts visually impaired users when approaching an intersection (Ihejimba and Wenkstern 2020). This work presents some limitations given the rough precision provided by GPS at ground level, especially at an intersection with several crossing directions. Other authors evaluated the feasibility of machine learning algorithms to identify traffic lights and track their status so that the visually impaired user can cross the road safely (Ghilardi et al. 2018). Furthermore, custom hardware implementations that apply image processing and machine learning to identify and track traffic lights have been reported in (Li et al. 2019). Information and feedback are provided to the visually impaired user through a voice synthesizer. Another similar version is based on a mobile phone device that must be held horizontally when crossing the street (Yu, Lee, and Kim 2019). The user is provided with guidance from the vibrations and sounds of the device. (Cheng et al. 2017) presented an assistive solution for crosswalk detection, consisting of an adaptive extraction and consistency analysis algorithm and a wearable navigation system (Intoer) for people with visual impairments. To achieve a more robust proposal, the authors considered several crosswalk situations that users can encounter: crosswalks at far distances from the user, crosswalks at close distances from the user, and crosswalks in front of the user. In addition, field tests were performed considering different variables, e2062818-2806 A. MONTANHA ET AL. such as time of day and weather. (Cheng et al. 2018) proposed a real-time Pedestrian Crossing Lights (PCL) detection algorithm using machine learning. The novelty of this work is based on its high precision and recall, robustness, and low complexity. Additionally, the authors applied a temporal-spatial analysis to track the detected PCL in order to improve the system’s results in challenging scenarios. Communication between the user and the device, consisting of an Intel RealSense R200 camera, a pair of bone-conduction earphones and a portable PC, is achieved through voice instructions. (Tian et al. 2021) described an assistive solution to understand crosswalk scenes and provide users with key information and directions to cross the street. The device consists of a head-mounted mobile equipped with an Intel RealSense camera and a mobile phone. In their solution, the authors carried out field tests on crosswalk detection, pedestrian traffic light recognition, and distance measurement, and proved their practical usability. One of their main contributions is the SensingAI dataset with more than 11,700 images for the recognition of pedestrian traffic lights, key objects on the crossroads, and the crosswalk. (Yang et al. 2018) described a real-world framework using a wearable device to help visually impaired pedestrians with navigation tasks. Specifically, they study the context situation at an intersection by detecting the crosswalk, traffic light, pedestrian and vehicle. The authors carried out comprehensive experiments as part of their framework, comparing their own approach with other algorithms against several datasets. The difficulties of visually impaired people experience using mobile applications have been studied (Qureshi and Wong 2019). The application of the user-centered design (UCD) philosophy is especially important for “not mainstream” user groups, and the resulting products have high acceptance rates (Krajnc, Feiner, and Schmidt 2010). Accessibility makes user interfaces perceivable, operable and understandable by people with a wide range of abilities and people in a wide range of circumstances, environments, and conditions. Therefore, accessibility also benefits people with disabilities and organizations that develop accessible products (Henry 2007). In the literature, an increasing number of crosswalk assistive solutions for visually impaired pedestrians have been reported. Most of these advances are possible due to the increasingly higher processing power of devices, computer vision techniques, and different wearable devices and smartphones. Our solution also benefits from this aspect; however, in our case, the user only needs a smartphone. To our knowledge, this work is the first to report an ecosystem in which traffic alterations can be performed in favor of visually impaired pedestrians. This work adds to our previous work in which the signal trilateration technique is proposed (Montanha et al. 2016). Compared to our previous work, the current proposed system incorporates the ability to segment visually impaired pedestrians automatically and in real time. In the previous version, they could not be distinguished from the rest of the pedestrians, so personalized APPLIED ARTIFICIAL INTELLIGENCE e2062818-2807 guidance could not be provided to cross the street. The previous version of the system could detect the presence of users and inform users carrying the smartphone application about the color of the traffic light. This work incorporates the visually impaired pedestrian segmentation technique, which allows the system to guide and assist them when crossing the street for the entire duration of the process. The app was designed and developed following the UCD approach. Pedestrian Tracking Proposal In this section, a proposal to detect and track pedestrians will be explored. The method implements a heuristic to estimate the pedestrian’s location using a signal trilateration process and a deep learning model. Signal Location Method to Support Pedestrian Tracking The system establishes a communication canal between the smart traffic lights and the application installed on the smartphone of the visually impaired user. This is a wireless communication achieved via WiFi signals between the two parts. At least three WiFi antennas are placed at an intersection, as can be seen in Figure 1. Figure 1. Proposal of camera and smart traffic lights installation in an intersection. e2062818-2808 A. MONTANHA ET AL. The smart traffic lights continuously monitor intersections using IP cameras. When a visually impaired pedestrian (with the mobile application opened) is detected to be within the WiFi range of at least one of the antennas, the localization process is requested. The system starts to compute a signal trilateration technique to identify in which quadrant the user is located (Montanha, Polidorio, and Romero-Ternero 2021). We explain this technique in detail in one of our previous works (Montanha, Polidorio, and Romero- Ternero 2021). The process continues when the user is located (the system detects in which quadrant they are). We will refer to this quadrant as the monitoring area. A processing thread is started for each pedestrian (visually impaired or not) that is located within the range of the monitoring areas, as they are being monitored by the cameras at the traffic light. The images captured from the monitoring area are further submitted to a pre-trained model whose function is to search for traces that identify pedestrians. This technique will be explained in more detail in the next section. Neural Network Pre-trained Model to Support Pedestrian Tracking The proposed system uses a pre-trained deep neural network model from the open-source toolkit OpenVINO 2 (Open Visual Inference and Neural Network Optimization), offered by Intel. The OpenVINO toolkit allows one to quickly develop and deploy a wide range of computer vision applications. The pretrained model used, person-detection-retail-0013, is part of the Object Detection Models group of Intel’s Pre-Trained Models. This model is based on the MobileNetV2-like backbone that includes depth-wise convolutions to reduce the amount of computation for the 3 × 3 convolution block. The single SSD head from 1/16 scale feature map has 12 clustered prior boxes. The model has an area under the precision/recall curve of 88.62%. The system implementing the neural network is capable of performing inference at a rate of one frame per second using the pre-trained model. The dimensions of the image are 640 × 480 pixels, and the color is RGB using codec H.265. The image format is MJPEG. In the proposed solution, the information detailed below is collected by the pre-trained algorithms at a once-per-second acquisition rate: image data, the position relative to the image, date and time of the event, dimensions of the segmented pedestrian, and predominant colors. The image data is stored using the Base64 encoding algorithm, which is commonly used to embed the image data within other formats, JSON in our case. Data Fusion for Signal Location and Image Processing Techniques Data obtained by signal trilateration and image processing techniques are combined to improve pedestrian segmentation precision. APPLIED ARTIFICIAL INTELLIGENCE e2062818-2809 In each processing thread, a statistical data correlation process is performed between the position of the detected pedestrian in the spatial plane calculated by the signal trilateration technique (xtri, ytri) and the relative position of the pedestrian in the image, obtained from the image processing algorithm (ximg, yimg). Both pairs of coordinates refer to the pedestrian’s location. If a correlation is established, the system then starts to look for the reidentification of the individual in the next frame to confirm the segmentation. The Pearson correlation coefficient is used because it measures the strength of the linear relationship between two variables. Correlation analysis is calculated in the R programming language and the pacman package. As input, the script receives a .csv file generated by the algorithm. The following variables are stored in the .csv: date and time, trilateration ID, trilateration coordinates (xtri, ytri), pedestrian ID, pedestrian pixel coordinates in the image (ximg, yimg), predominant color of the pedestrian image, height and width of the pedestrian. The system relies on the computation of the Pearson coefficient to guide the pedestrian through the street crossing process. Under certain circumstances (excess noise in the data, low visibility), it may occur that during the computation process a correlation is not found or is below the threshold of 90%. This implies that identification and tracking of the blind pedestrian are not possible, since we cannot merge information from cameras and signals. Since this is a possible event, an alternative procedure must be defined to allow the pedestrian to cross the street safely. The system prepares a contingency assistive procedure. This assistive procedure informs the visually impaired pedestrian about the status (color) of the traffic light. System Design and Architecture This section addresses the main components of the design and architecture of the system. There are three main hardware components in the proposed system: smartphone, smart traffic lights, and WiFi antennas. The smart traffic lights monitor intersections using IP cameras and computing the signal trilateration and image processing algorithms. Meanwhile, the WiFi antennas installed at the intersections are responsible for the exchange of the WiFi signal with the smartphone. An overview of the system is shown in Figure 2. There are two main software components: the computations running on the smart traffic light (signal trilateration technique, neural network-based image processing algorithm, data correlation process), and the mobile application. The transmission of information between the mobile application and the smart traffic light is carried out using high-speed cellular network technology such as 3 G or 4 G. WiFi signals are used solely for localization purposes. e2062818-2810 A. MONTANHA ET AL. As explained previously, the target user localization is first estimated in one of the quadrants (monitoring area) using our signal trilateration technique. Then, the image processing technique is added to the localization system, to segment the target user from the rest of the pedestrians. The images captured by the IP cameras from the monitoring areas are submitted to the pre-trained model. For each segmented pedestrian, the system stores information in a buffer. Each buffer has a space for analysis of 100 frames. Once the buffer is filled, the system starts the data fusion method. Once a correlation is established, the system then starts to search for the reidentification of the individual in the next frames to confirm the segmentation of the visually impaired pedestrian. OpenCV version 4.5.2 and Python 3 have been used to capture and process the images on a Debian 10 Linux distribution (Buster version). The data resulting from the correlation process between the signal trilateration data and the image processing data need to exceed the selected threshold of 90% to start the assistance process. In addition, information from the traffic situation is checked, adding the following conditions to be met before initiating safe crossing: 1) the traffic light is closed for vehicles on the lane; 2) there are no vehicles on the lane, or, if there are, the first line of vehicles are stopped, waiting for the traffic light to turn green. If the traffic light is red for vehicles but there are vehicles moving in the lane, at high or low speeds, the system inspects the area closest to the crosswalk. If there is no line of stopped vehicles in front of the crosswalk, the system does not send instructions for crossing (we need to assume that a car could not stop at a red light). Information is sent to the mobile application, alerting about the status of the traffic lights and the presence of vehicles in motion. The interaction between the mobile application interface and the user is explained in more detail in Section 4.2. In addition, smart traffic lights can increase the duration of the Figure 2. Overview of the system design for the proposed mobile assistive system. APPLIED ARTIFICIAL INTELLIGENCE e2062818-2811 pedestrians). The real-world pilot study has shown great results; however, we are aware that our research presents several limitations. There is room to improve the accuracy of the system in more extreme circumstances, such as low visibility due to weather conditions or nighttime. Second, the performance and usage data could have been collected over a longer period to develop statistical information. Another drawback of the proposal could be that the system is highly dependent on the hardware installation. However, we consider this to be beneficial for the user who only needs to carry a smartphone. A usability problem was detected in situations where there is noise pollution due to heavy traffic or construction near the site. It can be difficult for the user to hear the instructions in this situation, so we are continuing to investigate alternative methods. Lastly, an analysis of different variants of the approach could have been beneficial to the study. Although there are more solutions to include people with visual impairments in the urban environment, there are still obstacles to overcome. Specifically, for urban environments that aim to become inclusive smart cities. We believe that there is a need to design and implement technological solutions that are safe and useful to users, widely available, and economically viable. This system was built into the existing computational platform of the intelligent traffic lights in use. These traffic lights already perform the task of segmenting vehicles and reading license plates. At each intersection, these tasks typically consume 70% of the available processing power. To avoid saturating the processor and therefore offering some level of slowness, there is an additional processing limit that can be used by additional resources to the primary tasks of the smart semaphore. For this reason, it is not possible to carry out more than three simultaneous processes of segmentation of people with visual impairments at the same intersection, and a fourth request would enter the process queue. If this happens, the system activates the emergency system and puts plan B into practice, notifying the user who cannot perform the assisted crossing. Conclusion In the context of AI-based technology, this work contributes to the development of assistive technology for the mobility of visually impaired pedestrians, specifically for street crossings. To our knowledge, this is the first study designed to aid visually impaired pedestrian street crossings that combines the use of smart traffic lights as an element to detect traffic and pedestrian situation through sensors, image processing, AI techniques, and mobile devices. This solution is based on the paradigms of Smart Cities and Internet of Things, seeking the fusion of data and the use of intelligent infrastructure. The functionality of the existing smart traffic light has been extended by adding WiFi antennas and developing segmentation software. This system as a whole can carry out a great number of tasks: acquire and transmit real-time images of their coverage locations to a traffic control center, communicate with pedestrians in e2062818-2818 A. MONTANHA ET AL. possession of mobile devices or devices installed in vehicles via WiFi and/or Bluetooth signals, assistive crossing for visually impaired pedestrians, among many other traffic regulation tasks. Furthermore, the data collection performed by this device contributes to the creation of a training database. We can conclude that the proposed solution adds as an innovating factor the use of existing infrastructure to deploy assistive technology for visually impaired pedestrians. Often, solutions to assist visually impaired people on a street crossing require the use of a portable device (in the form of glasses or a stick). We can see that there are benefits to a solution that limits the use of a smartphone on the user’s end and relies more heavily on a common infrastructure facilitated by a smart city context. Furthermore, communication between assistive software and traffic regulation software can be beneficial for visually impaired users, as the traffic light can adapt to the traffic situation to ensure safe crossing. Future Work Further research is required to improve the functionality of the system at night. Without adequate light, most of the time the system is unable to segment the visually impaired pedestrian, and the emergency assistive system has to be applied. Our results are encouraging, so further development of techniques and algorithms to improve accuracy can be carried out within the scientific community. As a continuation of this work, open-source code can be released, making it available on GitHub, so researchers around the world can contribute to the code and implement the system in their cities. Notes 1. https://seebot.com.br/es/agent.html 2. https://docs.openvinotoolkit.org 3. https://seebot.com.br/es/agent.html Disclosure Statement No potential conflict of interest was reported by the author(s). ORCID Aleksandro Montanha http://orcid.org/0000-0002-6288-2753 Andreea M. Oprescu http://orcid.org/0000-0003-3308-4688 MCarmen Romero-Ternero http://orcid.org/0000-0001-6965-9485 APPLIED ARTIFICIAL INTELLIGENCE e2062818-2819 References Balasuriya, B. K., N. P. Lokuhettiarachchi, A. R. M. D. N. Ranasinghe, K. D. C. Shiwantha, and C. Jayawardena. 2017. 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