A survey of video processing techniques for traffic applications

V Kastrinaki, M Zervakis, K Kalaitzakis
2003 Image and Vision Computing  
Video sensors become particularly important in traffic applications mainly due to their fast response, easy installation, operation and maintenance, and their ability to monitor wide areas. Research in several fields of traffic applications has resulted in a wealth of video processing and analysis methods. Two of the most demanding and widely studied applications relate to traffic monitoring and automatic vehicle guidance. In general, systems developed for these areas must integrate, amongst
more » ... ir other tasks, the analysis of their static environment (automatic lane finding) and the detection of static or moving obstacles (object detection) within their space of interest. In this paper we present an overview of image processing and analysis tools used in these applications and we relate these tools with complete systems developed for specific traffic applications. More specifically, we categorize processing methods based on the intrinsic organization of their input data (feature-driven, area-driven, or model-based) and the domain of processing (spatial/frame or temporal/video). Furthermore, we discriminate between the cases of static and mobile camera. Based on this categorization of processing tools, we present representative systems that have been deployed for operation. Thus, the purpose of the paper is threefold. First, to classify image-processing methods used in traffic applications. Second, to provide the advantages and disadvantages of these algorithms. Third, from this integrated consideration, to attempt an evaluation of shortcomings and general needs in this field of active research. q (M. Zervakis). V. Kastrinaki et al. / Image and Vision Computing 21 (2003) 359-381 Table 2 (continued) System Operating domain Processing techniques Major applications LANA [24] † Spatial processing † Model-driven approach exploiting features to compute likelihoud † Automatic lane finding † DCT features † Moving camera † Deformable template models for priors LOIS [9] † Spatial processing † Model-driven approach with deformable templates for edge matching † Automatic lane finding † Moving camera CLARK [108] † Spatial processing of images † LOIS for lane detection † Automatic lane finding and obstacle detection † Temporal estimation of range observations † Color and deformable templates for object detection † Moving camera PVS and AHVS [95] † Spatial processing † Feature-driven approach † Automatic lane finding Using edge detection † Moving camera RALPH [109] † Spatial processing † Feature-driven approach † Automatic lane finding † Stereo images using edge orientation † Moving camera † Mapping of left to right image features ROMA [19] † Spatial operation with tempo-ral projection of lane location † Feature-driven approach † Automatic lane finding Using edge orientation † static camera SIDE WALK [110] † Spatial processing † Lane-region detection † Automatic lane finding † via Thresholding for area segmentation † Moving camera SCARF [111] † Spatial processing † Model-driven approach † Automatic lane following † Using stochastic modeling for image segmentation † Moving camera CAPC [10] † Spatial-domain lane finding with temporal projection of lane location † Feature-driven approach † Automatic lane following † Temporal estimation of vehicle's state variables † Using edge detection and constraints on model for lane width and lane spacing † Moving camera ALV [12] † Spatial-domain lane and object detection † Lane-region detection for ALF using color classification † Automatic lane following † Temporal estimation of vehicle's state variables † Spatial signature for object detection via color segment. † Moving camera NAVLAB [11] † Spatial-domain lane finding † Lane-region detection for alf via color and texture classification † Automatic lane following † Temporal estimation of vehicle's state variables for 3D road-geometry estimation and projection of frame-to-world coordinates † Moving camera ALVINN and MANIAC [112] † Spatial-processing with neural nets † Recognition of space signature of road through neural nets † Automatic lane following † Moving camera † Form of temporal matching Ref. [3] † Spatial-processing † Model-driven approach † Automatic lane following † Spatio-temporal processing for ALF and vehicle guidance † Model-driven approach † Autonomous vehicle guidance † Temporal estimation of vehicle's state variable † 3D object modeling and forward perspective mapping † Moving camera † State-variable estimation of road skeletal lines for Alf † State-variable estimation of 3D model structure for object detection UTA [14] † Spatio-temporal processing for ALF and object detection † Feature-driven approach † Autonomous vehicle guidance Based on neural networks † Use of spatio-temporal signature † Moving camera Ref. [14] † Spatial processing for alf and object detection † Feature-driven approach † Autonomous vehicle guidance † Feature tracking in temporal domain † Color road detection and lane detection via RLS fitting † Moving camera † Interframe differencing and edge detection for locating potential object templates † Feature tracking via RLS
doi:10.1016/s0262-8856(03)00004-0 fatcat:nr3mmyj55jaxxljkvst4laivxy