Road Detection in Traffic Analysis: A Context-aware Approach

Abstract

Correctly identifying the road area on an image is a crucial task for many traffic analyses based on surveillance cameras and computer vision. Despite that, most of the systems do not provide this functionality in an automatic fashion; instead, the road area needs to be annotated by tedious and inefficient manual processes. This situation results in further inconveniences when one deals with a lot of cameras, demanding considerable effort to setup the system. Besides, since traffic analysis is an outdoor activity, cameras are exposed to disturbances due to natural events (e.g., wind, rain and bird strikes), which may require recurrent system reconfiguration. Although there are some solutions intended to provide automatic road detection, they are not capable of dealing with common situations in urban context, such as poorly-structured roads or occlusions due to objects stopped in the scene. Moreover in many cases they are restricted to straight-shaped roads (commonly freeways or highways), so that automatic road detection cannot be provided in most of the traffic scenarios. In order to cope with this problem, we propose a new approach for road detection. Our method is based on a set of innovative solutions, each of them intended to address specific problems related to the detection task. In this sense, a context-aware background modeling method has been developed, which extracts contextual information from the scene in order to produce background models more robust to occlusions. From this point, segmentation is performed to extract the shape of each object in the image; this is accomplished by means of a superpixel method specially designed for road segmentation, which allows for detection of roads with any shape. For each extracted segment we then compute a set of features, the goal of which is supporting a decision tree-based classifier in the task of assigning the objects as being road or non-road. The formulation of our method — a road detection carried out by a combination of multiple features — makes it able to deal with situations where the road is not easily distinguishable from other objects in the image, as when the road is poorly-structured. A thorough evaluation has indicated promising results in favour of this method. Quantitatively, the results point to 75% of accuracy, 90% of precision and 82% of recall over challenging traffic videos caught in non-controlled conditions. Qualitatively, resulting images demonstrate the potential of the method to perform road detection in different situations, in many cases obtaining quasi-perfect results.