Overtaking Vehicle Detection Techniques based on Optical Flow and Convolutional Neural Network

Computer Vision Applications in Intelligent Transportation Systems: A Survey

“…Variants of CNN networks are widely used in CV studies in the field of ITS. There are a number of CNN-based studies in the literature, such as those focused on automatic license plate recognition [ 40 , 41 ], traffic sign detection and recognition [ 25 , 42 , 43 , 44 , 45 , 46 , 47 , 48 , 49 , 50 , 51 ], vehicle detection [ 52 , 53 , 54 , 55 ], pedestrian detection [ 56 , 57 , 58 , 59 , 60 ], lane line detection [ 61 , 62 , 63 ], obstacle detection [ 64 ], video anomaly detection [ 65 , 66 , 67 , 68 ], structural damage detection [ 69 , 70 , 71 , 72 , 73 , 74 , 75 , 76 , 77 , 78 ], and steering angle detection [ 79 , 80 , 81 , 82 ] in autonomous vehicles. The most popular and advanced CNN-based architectures in the literature [ 83 , 84 ] are presented in Figure 3 .…”

“…Hybrid methods include a combination of multiple ML or DL methods used in CV techniques. There are many intelligent transportation applications for this approach, such as license plate recognition [ 85 , 100 , 101 ], video anomaly detection [ 68 , 89 , 92 , 102 ], automatic license plate recognition [ 25 , 103 ], vehicle detection [ 11 , 12 , 53 , 55 ], pedestrian detection [ 58 , 104 ], lane line detection [ 63 , 105 ], obstacle detection [ 106 , 107 , 108 , 109 , 110 ], structural damage detection [ 111 , 112 , 113 ], and autonomous vehicle applications [ 13 , 114 , 115 ].…”

“…Camera systems with different types of sensors are employed to detect and classify objects in the environment [ 39 ]. The issue of vehicle recognition in autonomous vehicles has been explored by many researchers, including Lange et al [ 39 ], Du et al [ 52 ], and Wu and Lin [ 53 ]. Light detection and ranging (LIDAR) sensors were used in [ 39 ] to identify areas containing potential vehicles.…”

“…Light detection and ranging (LIDAR) sensors were used in [ 39 ] to identify areas containing potential vehicles. In order to detect moving vehicles, a fixed number of tracking points were determined in a certain region in [ 53 ], after which the vehicles were detected from the movement clues. In [ 159 ], the authors focused on the development of a video analysis processor for the detection and classification of vehicles in urban areas, adopting a fuzzy-set-based approach.…”