Comparison of vehicle re-identification models for trucks based on axle spacing measurements

Basar, Gulsevi; Cetin, Mecit; Nichols, Andrew P

doi:10.1080/15472450.2018.1441027

Cited by 9 publications

(7 citation statements)

References 29 publications

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“…Vehicle features are extracted and fused at inference to rank the gallery vehicle images for a given query. The authors in [65] compared vehicle re-identification models for trucks utilizing axle spacing and vehicle lengths. They formulated the re-identification as a mathematical assignment problem where models such as Naïve Bayes, Bayesian models, and Gaussian mixture models.…”

Section: Related Workmentioning

confidence: 99%

Enhanced Vehicle Re-Identification for Smart City Applications Using Zone Specific Surveillance

et al. 2023

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Vehicle re-identification is an important feature of an intelligent transportation system as part of a smart city application. Vehicle re-identification aims at matching vehicles from images acquired by surveillance cameras at different locations. During rush hours, vehicles are densely occupied across regions such as entry/exit of gated campuses, railways, airports, educational institutions, etc. Due to this uneven flow of traffic, there is a possibility of violation of traffic rules by the vehicles that lead to a security breach. In such scenarios to speed up the re-identification process, it is justified to look into a specific group of surveillance cameras to detect and re-identify vehicles on day to day basis in near real-time. However, the existing vehicle re-identification datasets do not contain zone specific information and therefore can not be used to evaluate the performance of re-identification algorithms in different zones. In the proposed work for re-identification, a framework is developed that performs vehicle re-identification across a group of cameras that monitors varying traffic movements over an area. These areas defined as "strategic zones" comprise a subset of non-overlapping cameras that are installed to monitor non-uniform vehicle movements. The reidentification framework is evaluated on a novel dataset developed to understand the performance of vehicle re-identification across strategic zones. The dataset consists of videos of vehicles captured through 20 CCTV surveillance cameras that are grouped into four different zones. Various experiments are conducted to study the performance of re-identification across four zones using a deep neural network with triplet loss, L2 regularization, and re-ranking. The experiments conducted with an image dimension of 224 × 224 have demonstrated an overall mAP of 77.22%. Also, for each of the four zones a mAP of 82.16%, 69.1%, 66.5%, and 75.76% is achieved. The experimental results demonstrate huge variations in the accuracy of vehicle re-identification method across different zones. Therefore, the study assess the possible measures that can be taken to improve the performance in individual zones for an accurate vehicle re-identification in intelligent transport system.

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Section: Related Workmentioning

confidence: 99%

Enhanced Vehicle Re-Identification for Smart City Applications Using Zone Specific Surveillance

et al. 2023

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“…𝜇 ̃𝑙,𝑡 = 𝜇 𝑙,𝑡−1 + 𝛽 𝜇 (𝜇 𝑙,𝑡−1 − 𝜇 ̃𝑙,𝑡−1 ) ( 16) where 𝜇 ̃𝑙,𝑡−1 and 𝜇 𝑙,𝑡−1 respectively are predicted and estimated means at the last time period 𝑡 − 1, and 𝛽 𝜇 is the smoothing parameter with respect to prediction error of mean travel time. This predicted mean is further adjusted according to the changes of spot speed as: Otherwise, 𝑇 ̃𝑙,𝑡 follows a lognormal distribution and its 𝛼 confidence interval can be calculated by (20).…”

Section: B Step 2 Lane-based Travel Time Window Constructionmentioning

confidence: 99%

“…As an anonymous way, vehicle re-identification (V-ReID) technique recently attracts much attention [10,[16][17][18][19][20][21]. Fig.…”

mentioning

confidence: 99%

Vehicle Re-identification for Lane-level Travel Time Estimations on Congested Urban Road Networks Using Video Images

Zhang

Chen

Lam

et al. 2022

IEEE Trans. Intell. Transport. Syst.

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The provision of lane-level travel time information can enable accurate traffic control and route guidance in urban roads with distinctive traffic conditions among lanes. However, few studies in the literature have been conducted to estimate lanelevel travel time distributions. This study proposes a new vehicle re-identification (V-ReID) method for estimating lane-level travel time distributions using video images from widely deployed surveillance cameras. In the proposed method, a lane-based bipartite graph matching is introduced to obtain optimal matches between upstream and downstream vehicles by considering lanelevel traffic conditions and vehicles' lane changing behaviors and visual features. A lane-based travel time estimation technique is introduced to real-time estimate full spectrum of lane-level distribution parameters, including not only the mean but also the standard deviation and the distribution type. A comprehensive case study is carried out on a congested urban road in Hong Kong. Results of case study show that the proposed method outperforms the state-of-the-art link-based V-ReID method and is capable for providing accurate lane-level travel time distribution information on congested urban roads. Index Terms-Vehicle re-identification, lane-level travel time, lane changing behaviors, video images. I. INTRODUCTION CCURATE and updated travel time information is a crucial requirement for many traffic monitoring, traffic Manuscript

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“…Cross-dock is a common logistics method used in transportation platforms to synchronize incoming and outgoing trucks to avoid holding up space between docks. This action plays an important role in accelerating the transportation flow (Chargui et al , 2022; Basar et al , 2018). It should be mentioned that cargo transportation at cross-dock terminals is a complex planning task including several activities such as unloading cargo delivered by inbound truck, sorting according to the designated destination and loading onto outbound truck.…”

Section: Introductionmentioning

confidence: 99%

A multi-objective optimization model of truck scheduling problem using cross-dock in supply chain management: NSGA-II and NRGA

Haghgoei,

Irajpour,

Hamidi

2024

JM2

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Purpose This paper aims to develop a multi-objective problem for scheduling the operations of trucks entering and exiting cross-docks where the number of unloaded or loaded products by trucks is fuzzy logistic. The first objective function minimizes the maximum time to receive the products. The second objective function minimizes the emission cost of trucks. Finally, the third objective function minimizes the number of trucks assigned to the entrance and exit doors. Design/methodology/approach Two steps are implemented to validate and modify the proposed model. In the first step, two random numerical examples in small dimensions were solved by GAMS software with min-max objective function as well as genetic algorithms (GA) and particle swarm optimization. In the second step, due to the increasing dimensions of the problem and computational complexity, the problem in question is part of the NP-Hard problem, and therefore multi-objective meta-heuristic algorithms are used along with validation and parameter adjustment. Findings Therefore, non-dominated sorting genetic algorithm (NSGA-II) and non-dominated ranking genetic algorithm (NRGA) are used to solve 30 random problems in high dimensions. Then, the algorithms were ranked using the TOPSIS method for each problem according to the results obtained from the evaluation criteria. The analysis of the results confirms the applicability of the proposed model and solution methods. Originality/value This paper proposes mathematical model of truck scheduling for a real problem, including cross-docks that play an essential role in supply chains, as they could reduce order delivery time, inventory holding costs and shipping costs. To solve the proposed multi-objective mathematical model, as the problem is NP-hard, multi-objective meta-heuristic algorithms are used along with validation and parameter adjustment. Therefore, NSGA-II and NRGA are used to solve 30 random problems in high dimensions.

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Comparison of vehicle re-identification models for trucks based on axle spacing measurements

Cited by 9 publications

References 29 publications

Enhanced Vehicle Re-Identification for Smart City Applications Using Zone Specific Surveillance

Enhanced Vehicle Re-Identification for Smart City Applications Using Zone Specific Surveillance

Vehicle Re-identification for Lane-level Travel Time Estimations on Congested Urban Road Networks Using Video Images

A multi-objective optimization model of truck scheduling problem using cross-dock in supply chain management: NSGA-II and NRGA

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