Video‐based traffic data collection system for multiple vehicle types

Abstract

Traffic data of multiple vehicle types are important for pavement design, traffic operations and traffic control. A new video-based traffic data collection system for multiple vehicle types is developed. By tracking and classifying every passing vehicle under mixed traffic conditions, the type and speed of every passing vehicle are recognised. Finally, the flows and mean speeds of multiple vehicle types are output. A colour image-based adaptive background subtraction is proposed to obtain more accurate vehicle objects, and a series of processes like shadow removal and setting road detection region are used to improve the system robustness. In order to improve the accuracy of vehicle counting, the cross-lane vehicles are detected and repeated counting for one vehicle is avoided. In order to reduce the classification errors, the space ratio of the blob and data fusion are used to reduce the classification errors caused by vehicle occlusions. This system was tested under four different weather conditions. The accuracy of vehicle counting was 97.4% and the error of vehicle classification was 8.3%. The correlation coefficient of speeds detected by this system and radar gun was 0.898 and the mean absolute error of speed detection by this system was only 2.3 km/h.

A video-based real-time adaptive vehicle-counting system for urban roads

“…The results are obtained by calculating the average accuracy for different scenarios using systems listed as follows: 1: Chen et al for car detection [ 20 ]; 2: Chen et al [ 21 ]; 3: Lei et al [ 22 ]; 4: Pornpanomchai et al [ 23 ]; 5: Rodríguez and García [ 24 ]; 6: Mohana et al [ 25 ]; 7: Li et al [ 26 ]. The average accuracy of vehicle counting for our system reaches 99.29%, which surpasses that of all the other listed algorithms, which is not entirely unexpected.…”

Traffic Congestion Avoidance System Using Foreground Estimation and Cascade Classifier

“…The Broad Learning System is fast method in terms of the training time and testing accuracy. A shadow removal algorithm with the help of the color-based background subtraction model is developed in [26]. The foreground estimation model is used for the detection and counting of the vehicle are the entrance points [27].…”

Vehicle detection, counting and classification in various conditions

“…Vehicle counting methods usually specify an area and check if any vehicle enters this area [9,25,26]. Those investigations including vehicle classification extract shape-based features like length, width and area [25][26][27][28] and then use a classifier such as the k nearest neighbour method or a neural network to categorise the vehicles.…”