Probabilistic Fusion of Vehicle Features for Reidentification and Travel Time Estimation Using Video Image Data

Abstract

This paper proposes a probabilistic vehicle reidentification algorithm for estimating travel time using the image data provided by traffic surveillance cameras. Each vehicle is characterized by its color, type, and length, which are extracted from the video record using image processing techniques. A data fusion rule is introduced to combine these three features to generate a probabilistic measure for a reidentification (matching) decision. The vehicle-matching problem is then reformulated as a combinatorial problem and solved by a minimum-weight bipartite matching method. To reduce the computational time, the algorithm uses the potential availability of historic travel time data to define a potential time window for vehicle reidentification. This probabilistic approach does not require vehicle sequential information and hence allows vehicle reidentification across multiple lanes. The algorithm is tested on a 5-km section of the expressway system in Bangkok, Thailand. The travel time estimation result is also compared with the directly observed data.

Automatic Freeway Incident Detection for Free Flow Conditions: A Vehicle Reidentification Based Approach Using Image Data from Sparsely Distributed Video Cameras

“…Thus, the vehicle type/shape feature is a vector that consists of the similarity score for each template. Detailed implementation of the VIP systems to traffic data extraction can be found in [24]. A formal description of the dataset obtained from the video record is presented in the following subsection.…”

“…To further consider the noise and uncertainty of vehicle signature, Kwong et al [23] introduced a statistical matching method in which the vehicle signature is treated as a random variable, and a probabilistic measure is calculated for matching decision making. During the past few years, the authors also developed a VRI system [24,25] by utilizing the emerging video image processing systems [26]. Various detailed vehicle features (e.g., vehicle color, length, and type) were extracted and a probabilistic data fusion rule was then introduced to combine these features to generate a matching probability (i.e., posterior probability) for reidentification purpose.…”

“…To this end, this paper aims to propose a VRI-based automatic incident detection algorithm under free flow condition. The revised VRI system is based on authors' previous work [24] with several major changes to cope with the purpose of incident detection, which eventually give rise to the incidentdetection-oriented VRI.…”

“…(i) Note that in the work of [24] a unified and fixed time window constraint is imposed on all the vehicles. However, the vehicles would maintain a relatively stable speed under free flow condition, which allows for the estimation of a flexible time window for each individual vehicle.…”

Traffic state estimation on highway: A comprehensive survey

“…Such connectivity is a highly technical problem; thus, it is out of scope for this article. For the details, see survey articles by Song and Lee (2013); Darwish and Abu Bakar (2015), dedicated to this and other VANET-specific problems.13 It matches a vehicle at distant locations based on the vehicle's features, such as license plate number(Kanayama et al, 1991), electromagnetic features of the vehicle's body(Coifman, 1998), and visual features(Sumalee et al, 2012) 14 E.g., media access control (MAC) address(Wasson et al, 2008;Barceló et al, 2010), CDR (call digital records)(Caceres et al, 2007;Wu et al, 2015) and similar data from mobile phones(Asakura and Hato, 2004))…”