Road Traffic Surveillance Research Articles

When the Profile Becomes the Population: Examining Privacy Governance and Road Traffic Surveillance in Canada and Australia

Use of automated licence/number plate recognition (‘ALPR/ANPR’) technologies in Canada and Australia raises significant policy questions for privacy advocates and criminal justice practitioners. The proliferation of mass surveillance through ALPR/ANPR also presents several conceptual puzzles about the links among criminal justice data flows, individual privacy and state responsibility in this actuarial age. In this article, we use case studies of ALPR/ANPR in Canada and Australia to examine privacy as a technique for governing road traffic surveillance. We explain our findings in light of Harcourt's (2007) argument against the use of actuarial prediction and ‘hit rates’ that are rationalised as the chief measure of law enforcement activities and effectiveness. Finally, we question the regulation of surveillance technologies such as ALPR/ANPR through current Canadian and Australian information privacy laws, with specific focus on privacy by design (‘PbD’), a strategy that favours improving law enforcement efficiency at the expense of privacy.

Lane mark segmentation and identification using statistical criteria on compressed video

The detection and localization of road lane marks are relevant to many applications of driving assistance and road traffic surveillance. Usually, these techniques work by processing all the pixels in every image, making the computational cost too hig

Interval Macroscopic Models for Traffic Networks

The development of real-time traffic models is of paramount importance for the purposes of optimizing traffic flow. Inspired by the compositional model (CM) and the METANET model, this paper proposes an interval approach for macroscopic traffic modeling. We develop an interval CM (ICM) and an interval implementation of the METANET model (IMETANET) that provide a natural way of predicting traffic flows without the assumption of uniform distribution of vehicles in a cell. The interval macroscopic models are suitable for real-time applications in road networks and can be part of road traffic surveillance and control systems. The performances of the interval approaches are investigated for both the ICM and the IMETANET models. The efficiency of the interval models is demonstrated over simulated data, and as well as over real traffic data from Motorway Incident Detection and Automatic Signalling (MIDAS) data sets from the United Kingdom.

Vehicle License Number Recognition System for Measuring Travel Time

A vehicle license number recognition system for measuring travel time has been developed. This is a vehicle license number recognition system by applying image processing. A series of four-digit numbers are transmitted to the center. With the recognition results being matched at the center, the system is capable of directly measuring travel time which is an important parameter for the operation of the road traffic surveillance and control system. This paper will discuss (1) a hardware composition for solving problems associated with the imaging processing to be done outdoors and problems such as processing speed, etc.; (2) the method of extracting each letter by extracting license plate region from complicated images, and the letter recognition system based on peripheral-pattern matching; and (3) experiments for evaluation in the field. As a result of experiments for evaluation, the processing time is within a second, and the recognition rate for all moving vehicles has been as high as over 85% at day time and night time.

Analogical representation of space and time

Abstract In computer vision, the usual level of ‘interpretation’ is the identification of the objects in the image. In this paper, we extend the level of interpretation to include spatial event detection using a knowledge base for a known scene. This will allow us to formulate a computational theory for forming conceptual descriptions about the behaviours of the objects. Here we describe an analogical representation of space and time that supports the formation of an event level description giving a local interpretation. These, in turn, can be used to form the global level conceptual descriptions. The analogical representation provides a characterization of the real world making explicit the behavioural data that is usually implicit. The knowledge base includes information about the scene layout and expected behaviours of the scene objects. Some of this behavioural information is spatially invariant and is used to contextually index the scene. The anlogical reasoning uses this contextual indexing of the spatial knowledge to provide a behavioural interpretation that describes what the objects are doing in the scene. We illustrate the approach with examples of road traffic surveillance at a German roundabout.