Automated Vehicle Detection and Classification

Abstract

Automated Vehicle Classification (AVC) based on vision sensors has received active attention from researchers, due to heightened security concerns in Intelligent Transportation Systems. In this work, we propose a categorization of AVC studies based on the granularity of classification, namely Vehicle Type Recognition, Vehicle Make Recognition, and Vehicle Make and Model Recognition. For each category of AVC systems, we present a comprehensive review and comparison of features extraction, global representation, and classification techniques. We also present the accuracy and speed-related performance metrics and discuss how they can be used to compare and evaluate different AVC works. The various datasets proposed over the years for AVC are also compared in light of the real-world challenges they represent, and those they do not. The major challenges involved in each category of AVC systems are presented, highlighting open problems in this area of research. Finally, we conclude by providing future directions of research in this area, paving the way toward efficient large-scale AVC systems. This survey shall help researchers interested in the area to analyze works completed so far in each category of AVC, focusing on techniques proposed for each module, and to chalk out strategies to enhance state-of-the-art technology.

Vehicle-Assisted Techniques for Health Monitoring of Bridges

“…In these methods the vehicle could be detected by passing through a fixed sensor, passing through the monitoring area, global coverage, or a hybrid of these methods [118][119][120]. Variety of information can be extracted using the sensors and detectors which may include vehicle count, shape-height, width and length- [121], speed [122], axle weight and spacing [123], acceleration/deceleration [124], make and model [125] and number plate [126].…”

Machine learning for geographically differentiated climate change mitigation in urban areas

“…ML is used to preprocess data from urban sensors and later to identify behavioral patterns of mobility and building users. For example, computer vision autonomously classifies and detects objects on images and videos (Boukerche, Siddiqui, & Mammeri, 2017); and thus computer vision techniques monitor traffic, automatically count vehicles and even derive socioeconomic information relying on data from street and road imagery (traffic cameras and Google Street View). For example, (Gebru et al, 2017) applied a convolutional neural network to street view images to classify motor vehicles encountered in particular neighborhoods, by this predicted income and voting patterns in neighborhoods across 200 US cities.…”

Mining Urban Traffic Condition from Crowd-Sourced Data

“…A prospective approach in ITS research is machine-learning-based vehicle detection over real-time images captured by existed camera systems. An Automated Vehicle Classification (AVC) system has three major modules or components, such as Features Extractor, Global Representation Generator, and Classifier [32]. Because the spreading of vision sensor systems around our city is too expensive, this approach does not solve the traffic problem completely.…”