Recent Advances in Vision-Based On-Road Behaviors Understanding: A Critical Survey.

On-road behavior analysis is a crucial and challenging problem in the autonomous driving vision-based area. Several endeavors have been proposed to deal with different related tasks and it has gained wide attention recently. Much of the excitement about on-road behavior understanding has been the labor of advancement witnessed in the fields of computer vision, machine, and deep learning. Remarkable achievements have been made in the Road Behavior Understanding area over the last years. This paper reviews 100+ papers of on-road behavior analysis related work in the light of the milestones achieved, spanning over the last 2 decades. This review paper provides the first attempt to draw smart mobility researchers' attention to the road behavior understanding field and its potential impact on road safety to the whole road agents such as: drivers, pedestrians, stuffs, etc. To push for an holistic understanding, we investigate the complementary relationships between different elementary tasks that we define as the main components of road behavior understanding to achieve a comprehensive understanding of approaches and techniques. For this, five related topics have been covered in this review, including situational awareness, driver-road interaction, road scene understanding, trajectories forecast, driving activities, and status analysis. This paper also reviews the contribution of deep learning approaches and makes an in-depth analysis of recent benchmarks as well, with a specific taxonomy that can help stakeholders in selecting their best-fit architecture. We also finally provide a comprehensive discussion leading us to identify novel research directions some of which have been implemented and validated in our current smart mobility research work. This paper presents the first survey of road behavior understanding-related work without overlap with existing reviews.

Deep Learning-Based Object Detection, Localisation and Tracking for Smart Wheelchair Healthcare Mobility.

This paper deals with the development of an Advanced Driver Assistance System (ADAS) for a smart electric wheelchair in order to improve the autonomy of disabled people. Our use case, built from a formal clinical study, is based on the detection, depth estimation, localization and tracking of objects in wheelchair's indoor environment, namely: door and door handles. The aim of this work is to provide a perception layer to the wheelchair, enabling this way the detection of these keypoints in its immediate surrounding, and constructing of a short lifespan semantic map. Firstly, we present an adaptation of the YOLOv3 object detection algorithm to our use case. Then, we present our depth estimation approach using an Intel RealSense camera. Finally, as a third and last step of our approach, we present our 3D object tracking approach based on the SORT algorithm. In order to validate all the developments, we have carried out different experiments in a controlled indoor environment. Detection, distance estimation and object tracking are experimented using our own dataset, which includes doors and door handles.

Deep Learning for Real-Time 3D Multi-Object Detection, Localisation, and Tracking: Application to Smart Mobility.

In core computer vision tasks, we have witnessed significant advances in object detection, localisation and tracking. However, there are currently no methods to detect, localize and track objects in road environments, and taking into account real-time constraints. In this paper, our objective is to develop a deep learning multi object detection and tracking technique applied to road smart mobility. Firstly, we propose an effective detector-based on YOLOv3 which we adapt to our context. Subsequently, to localize successfully the detected objects, we put forward an adaptive method aiming to extract 3D information, i.e., depth maps. To do so, a comparative study is carried out taking into account two approaches: Monodepth2 for monocular vision and MADNEt for stereoscopic vision. These approaches are then evaluated over datasets containing depth information in order to discern the best solution that performs better in real-time conditions. Object tracking is necessary in order to mitigate the risks of collisions. Unlike traditional tracking approaches which require target initialization beforehand, our approach consists of using information from object detection and distance estimation to initialize targets and to track them later. Expressly, we propose here to improve SORT approach for 3D object tracking. We introduce an extended Kalman filter to better estimate the position of objects. Extensive experiments carried out on KITTI dataset prove that our proposal outperforms state-of-the-art approches.

Robust radial face detection for omnidirectional vision.

Bio-inspired and non-conventional vision systems are highly researched topics. Among them, omnidirectional vision systems have demonstrated their ability to significantly improve the geometrical interpretation of scenes. However, few researchers have investigated how to perform object detection with such systems. The existing approaches require a geometrical transformation prior to the interpretation of the picture. In this paper, we investigate what must be taken into account and how to process omnidirectional images provided by the sensor. We focus our research on face detection and highlight the fact that particular attention should be paid to the descriptors in order to successfully perform face detection on omnidirectional images. We demonstrate that this choice is critical to obtaining high detection rates. Our results imply that the adaptation of existing object-detection frameworks, designed for perspective images, should be focused on the choice of appropriate image descriptors in the design of the object-detection pipeline.

Tensile strength of biological fibrin sealants: a comparative study.

BACKGROUND: Fibrin sealants are commonly used in liver surgery, although their effectiveness in routine clinical practice remains controversial. Individual sealant characteristics are based on hemostatic effects and adhesion properties that can be experimentally measured using the 'rat skin test' or the 'pig skin test'. This study used a more relevant and realistic experimental canine model to compare the differences in the adhesive properties of four fibrin sealants in hepatectomy: Tisseel/Tissucol, Tachosil, Quixil, and Beriplast. MATERIALS AND METHODS: A partial hepatectomy was performed in beagle dogs under general anesthesia to obtain liver cross-sections. Fibrin sealants were allocated to dog livers using a Youden square design. The tensile strength measurement was performed using a traction system to measure the rupture stress point of a small wooden cylinder bonded to the liver cross-section. RESULTS: Significantly greater adhesion properties were observed with Tisseel/Tissucol compared with Quixil or Beriplast (P = 0.002 and 0.001, respectively). Similarly, Tachosil demonstrated significantly greater adhesive properties compared with Beriplast (P = 0.009) or Quixil (P = 0.014). No significant differences were observed between Tisseel/Tissucol and Tachosil or between Beriplast and Quixil. CONCLUSIONS: The results of this comparative study demonstrate that different fibrin sealants exhibit different adhesive properties. Tisseel/Tissucol and Tachosil provided greatest adhesion to liver cross-section in our canine model of hepatectomy. These results may enable the optimal choice of fibrin sealants for this procedure in clinical practice.