An Effective Algorithm for Video‐Based Parking and Drop Event Detection

Abstract

Real-time and accurate detection of parking and dropping events on the road is important for the avoidance of traffic accidents. The existing algorithms for detection require accurate modeling of the background, and most of them use the characteristics of two-dimensional images such as area to distinguish the type of the target. However, these algorithms significantly depend on the background and are lack of accuracy on the type of distinction. Therefore, this paper proposes an algorithm for detecting parking and dropping objects that uses real three-dimensional information to distinguish the type of target. Firstly, an abnormal region is initially defined based on status change, when there is an object that did not exist before in the traffic scene. Secondly, the preliminary determination of the abnormal area is bidirectionally tracked to determine the area of parking and dropping objects, and the eight-neighbor seed filling algorithm is used to segment the parking and the dropping object area. Finally, a three-view recognition method based on inverse projection is proposed to distinguish the parking and dropping objects. The method is based on the matching of the three-dimensional structure of the vehicle body. In addition, the three-dimensional wireframe of the vehicle extracted by the back-projection can be used to match the structural model of the vehicle, and the vehicle model can be further identified. The 3D wireframe of the established vehicle is efficient and can meet the needs of real-time applications. And, based on experimental data collected in tunnels, highways, urban expressways, and rural roads, the proposed algorithm is verified. The results show that the algorithm can effectively detect the parking and dropping objects within different environment, with low miss and false detection rate.

A Survey of Methods and Technologies for Congestion Estimation Based on Multisource Data Fusion

“…There are several papers where researchers use inductive loops for vehicle reidentification and travel time estimation [49][50][51]. Cameras and video-and image-processing are used for collecting traffic data as point and point-to-point sensors [52][53][54][55].…”

Analysis of the System of Controlling Paid Parking Zones

“…The solution implemented in Warsaw is commonly referred to as e-control and is provided by SmartFactor [17]. In operation, this system makes the most of state-of-the-art IT technologies, such as machine vision [18], Internet of things (IoT) [19][20][21][22], advanced number plate detection algorithms [23,24], and plate number identification using selected machine learning components [24,25]. The system also uses different types of highly advanced measuring sensors, which enable determination of the distance to a parked vehicle by means of a lidar [26][27][28][29] or high-precision readout of GPS coordinates in order to establish the location [30,31] and date of the given control procedure.…”