POL-LWIR Vehicle Detection: Convolutional Neural Networks Meet Polarised Infrared Sensors

For vehicle autonomy, driver assistance and situational awareness, it is necessary to operate at day and night, and in all weather conditions. In particular, long wave infrared (LWIR) sensors that receive predominantly emitted radiation have the capability to operate at night as well as during the day. In this work, we employ a polarised LWIR (POL-LWIR) camera to acquire data from a mobile vehicle, to compare and contrast four different convolutional neural network (CNN) configurations to detect other vehicles in video sequences. We evaluate two distinct and promising approaches, two-stage detection (Faster-RCNN) and one-stage detection (SSD), in four different configurations. We also employ two different image decompositions: the first based on the polarisation ellipse and the second on the Stokes parameters themselves. To evaluate our approach, the experimental trials were quantified by mean average precision (mAP) and processing time, showing a clear trade-off between the two factors. For example, the best mAP result of 80.94% was achieved using Faster-RCNN, but at a frame rate of 6.4 fps. In contrast, MobileNet SSD achieved only 64.51% mAP, but at 53.4 fps.Comment: Computer Vision and Pattern Recognition Workshop 201

Aprendizaje evolutivo supervisado: Uso de histograma de gradiente y algoritmo de enjambre de partículas para detección y seguimiento de peatones en secuencia de imágenes infrarrojas

Recently, tracking and pedestrian detection from various images have become one of the major issues in the field of image processing and statistical identification.  In this regard, using evolutionary learning-based approaches to improve performance in different contexts can greatly influence the appropriate response.  There are problems with pedestrian tracking/identification, such as low accuracy for detection, high processing time, and uncertainty in response to answers.  Researchers are looking for new processing models that can accurately monitor one's position on the move.  In this study, a hybrid algorithm for the automatic detection of pedestrian position is presented.  It is worth noting that this method, contrary to the analysis of visible images, examines pedestrians' thermal and infrared components while walking and combines a neural network with maximum learning capability, wavelet kernel (Wavelet transform), and particle swarm optimization (PSO) to find parameters of learner model. Gradient histograms have a high effect on extracting features in infrared images.  As well, the neural network algorithm can achieve its goal (pedestrian detection and tracking) by maximizing learning.  The proposed method, despite the possibility of maximum learning, has a high speed in education, and results of various data sets in this field have been analyzed. The result indicates a negligible error in observing the infrared sequence of pedestrian movements, and it is suggested to use neural networks because of their precision and trying to boost the selection of their hyperparameters based on evolutionary algorithms

Safe Intelligent Driver Assistance System in V2X Communication Environments based on IoT

In the modern world, power and speed of cars have increased steadily, as traffic continued to increase. At the same time highway-related fatalities and injuries due to road incidents are constantly growing and safety problems come first. Therefore, the development of Driver Assistance Systems (DAS) has become a major issue. Numerous innovations, systems and technologies have been developed in order to improve road transportation and safety. Modern computer vision algorithms enable cars to understand the road environment with low miss rates. A number of Intelligent Transportation Systems (ITSs), Vehicle Ad-Hoc Networks (VANETs) have been applied in the different cities over the world. Recently, a new global paradigm, known as the Internet of Things (IoT) brings new idea to update the existing solutions. Vehicle-to-Infrastructure communication based on IoT technologies would be a next step in intelligent transportation for the future Internet-of-Vehicles (IoV). The overall purpose of this research was to come up with a scalable IoT solution for driver assistance, which allows to combine safety relevant information for a driver from different types of in-vehicle sensors, in-vehicle DAS, vehicle networks and driver`s gadgets. This study brushed up on the evolution and state-of-the-art of Vehicle Systems. Existing ITSs, VANETs and DASs were evaluated in the research. The study proposed a design approach for the future development of transport systems applying IoT paradigm to the transport safety applications in order to enable driver assistance become part of Internet of Vehicles (IoV). The research proposed the architecture of the Safe Intelligent DAS (SiDAS) based on IoT V2X communications in order to combine different types of data from different available devices and vehicle systems. The research proposed IoT ARM structure for SiDAS, data flow diagrams, protocols. The study proposes several IoT system structures for the vehicle-pedestrian and vehicle-vehicle collision prediction as case studies for the flexible SiDAS framework architecture. The research has demonstrated the significant increase in driver situation awareness by using IoT SiDAS, especially in NLOS conditions. Moreover, the time analysis, taking into account IoT, Cloud, LTE and DSRS latency, has been provided for different collision scenarios, in order to evaluate the overall system latency and ensure applicability for real-time driver emergency notification. Experimental results demonstrate that the proposed SiDAS improves traffic safety

Shaping the future through Artificial Intelligent technologies to reduce vehicle accidents in Abu Dhabi

Traffic accidents (TAs) constitute one of the top killers in the United Arab Emirates (UAE). Without practical approaches to address the TAs in Abu Dhabi, the region is likely to experience continued economic losses and health burden to the affected families and country. This study identified interventions and solutions for mitigating TAs in Abu Dhabi. The study was guided by research questions that focused on the causes of accidents and how to mitigate TA. The study was based on descriptive observational methodology where quantitative data was collected using a detailed survey questionnaire (n= 300) that assessed various aspects relating to the driver’s behaviour. The 2007 to 2017 MVC injuries baseline data were also analyzed. Data on TAs control strategies from existing studies were used to assess the artificial intelligent approaches in road safety management. The quantitative data analysis was carried out using SPSS software and Microsoft Excel software. The study findings showed that the most common traffic problems on Abu Dhabi's roads include driver-related factors, vehicular factors, and road condition-related factors. Risky overtaking, violation of the need to keep a safe distance and violation of speed limits were noted as the significant violations associated with the traffic problems on Abu Dhabi’s roads. The baseline data analysis findings indicated that the three regions in Abu Dhabi registered a general reduction in TAs over the 10 years (2007 to 2017). However, the reduction in Al Ain was minimal over the study period. The study’s findings relating to the forecasting of the accident trends showed that the Western region and Abu Dhabi would continue to experience a reduction in TAs in the future while the frequency of accidents in Al Ain will increase between 2017 and 2024. Most of the accidents in Abu Dhabi are associated with driver behaviour. The identified risky driver behaviours include the failure to keep adequate distance, maintain recommended speeds, and reckless driving. The study also noted the need to adopt artificial intelligent based interventions to limit the occurrence of accidents and enhance road safety. Based on the reported findings, management of the traffic problems need to focus on controlling risky driver behaviours. Road safety authorities in Abu Dhabi should adopt artificial intelligent approaches in the management of road safety