Danescu Radu, Itu Razvan, Petrovai Andra
Computer Science Department, Technical University of Cluj Napoca, 28 Memorandumului Street, Cluj Napoca 400114, Romania.
Sensors (Basel). 2016 Oct 17;16(10):1721. doi: 10.3390/s16101721.
The driving environment is complex and dynamic, and the attention of the driver is continuously challenged, therefore computer based assistance achieved by processing image and sensor data may increase traffic safety. While active sensors and stereovision have the advantage of obtaining 3D data directly, monocular vision is easy to set up, and can benefit from the increasing computational power of smart mobile devices, and from the fact that almost all of them come with an embedded camera. Several driving assistance application are available for mobile devices, but they are mostly targeted for simple scenarios and a limited range of obstacle shapes and poses. This paper presents a technique for generic, shape independent real-time obstacle detection for mobile devices, based on a dynamic, free form 3D representation of the environment: the particle based occupancy grid. Images acquired in real time from the smart mobile device's camera are processed by removing the perspective effect and segmenting the resulted bird-eye view image to identify candidate obstacle areas, which are then used to update the occupancy grid. The occupancy grid tracked cells are grouped into obstacles depicted as cuboids having position, size, orientation and speed. The easy to set up system is able to reliably detect most obstacles in urban traffic, and its measurement accuracy is comparable to a stereovision system.
驾驶环境复杂多变，驾驶员的注意力不断受到挑战，因此通过处理图像和传感器数据实现的基于计算机的辅助可能会提高交通安全。虽然有源传感器和立体视觉具有直接获取三维数据的优势，但单目视觉易于设置，并且可以受益于智能移动设备不断增强的计算能力，以及几乎所有智能移动设备都配备嵌入式摄像头这一事实。有几种驾驶辅助应用程序可供移动设备使用，但它们大多针对简单场景以及有限的障碍物形状和姿态范围。本文提出了一种基于环境的动态、自由形式三维表示（即基于粒子的占用栅格）的通用、形状无关的移动设备实时障碍物检测技术。从智能移动设备摄像头实时获取的图像经过处理，去除透视效果并分割得到的鸟瞰图图像，以识别候选障碍物区域，然后用于更新占用栅格。占用栅格中被跟踪的单元格被分组为障碍物，这些障碍物被描绘为具有位置、大小、方向和速度的长方体。该易于设置的系统能够可靠地检测城市交通中的大多数障碍物，其测量精度与立体视觉系统相当。
