Trajectory based vehicle counting and anomalous event visualization in smart cities

A secure road traffic congestion detection and notification concept based on V2I communications

“…Their system has a three-layer architecture that consists of a traffic flow policy model and a high-level coordination between the so-called intersection control agents. The authors in [3] proposed a real time vehicle detection and tracking system using surveillance videos. They proposed a video processing algorithm for vehicle detection and analysing traffic density on a particular lane.…”

“…Many studies on road traffic control problems address the efficiency aspect, for instance, proposing algorithms and methods for traffic lights to reduce congestion and CO2 emission (see, for example, the studies [1][2][3][4][5][6][7][8][9][10][11][12]). Besides the theoretical research studies, adaptive traffic control systems are already widely deployed on the roads, using technologies of induction loop or cameras to mitigate congestion.…”

Traffic Flow Detection at Road Intersections Based on K-Means and NURBS Trajectory Clustering

“…According to the different calculation methods and principles, there are currently four categories of traffic flow statistics: the target detection method [3][4][5][6], feature point motion trajectory clustering method [7][8][9], regional regression method [10,11] and density estimation method [12][13][14]. Seenouvong et al [3] completed the vehicle count in the set area by combining background subtraction with morphological filtering, with an accuracy of 96%; Memon et al [4] introduced the Gaussian mixture model (GMM) to quickly detect and count vehicles in the field of view and realized the self-classification of moving targets by contour comparison; Chen [5] proposed to use the B-spline curve method to obtain the vehicle area, so as to realize the vehicle count; Leonel et al [6] proposed a video sequence vehicle counting method based on augmented quantum space learning.…”

“…e accuracy rate of counting vehicles at an average speed of 26 frames per second is 96.6%, and it can well deal with camera shake and sudden illumination changes caused by the environment and automatic camera exposure. Rabbouch et al [7] designed a pattern recognition system based on unsupervised clustering of tracks, which realized intelligent detection, counting, and recognition of traffic targets; Mehboob et al [8] built a real-time system through analyzing the temporal and spatial characteristics of vehicle trajectory, which it can deduce and track the target behavior online, and joint Hungarian tracking algorithm to count the number of vehicles; in terms of vehicle flow statistics by different types, Rauf et al [9] proposed a method based on target tracking and convolutional neural network transfer learning; the regional regression method is well reflected in the literature [10,11]. Liang et al [10] constructed a regression model by extracting the edge features and gradient features of vehicles on the highway to obtain the vehicle flow parameters; Chen [11] suggested a hierarchical classification-based regression model for accurate vehicle counting in view of the complex and changeable characteristics of the actual traffic scene; Lempitsky and Zisserman [12] proposed the target counting algorithm framework based on density estimation in 2010, which is the most common and concerned counting framework at present.…”

Comparing Sparse and Dense Optical Flow Methods to Detect Traffic Anomalies, Based on Orientation

“…By detecting traffic violations, the probability of traffic accidents and the severity of them can be reduced [2,22,32]. For automatic traffic anomaly detection, a majority of systems deploys optical flow features to analyze orientation, velocity, appearance, and density of moving vehicles [8,14,17,19,25,28]. The features extracted could be used to detect anomalies by using various machine learning methods, such as K-nearest neighbor [25] or One-Class Support Vector Machine [1,14,31].…”