Vehicle Identity Recovery for Automatic Number Plate Recognition Data via Heterogeneous Network Embedding

Abstract

Automatic number plate recognition (ANPR) systems, which have been widely equipped in many cities, produce numerous travel data for intelligent and sustainable transportation. ANPR data operate at an individual level and carry the unique identities of vehicles, which can support personalized traffic planning. However, these systems also suffer from the common problem of missing data. Different from the traditional missing cases, we focus on the problem of the loss of vehicle identities in ANPR records due to recognition failure or other environmental factors. To address the issue, we propose a heterogeneous graph embedding framework that constructs a travel heterogeneous information network (THIN) and learns the embeddings of the entities to find the best matched vehicles for the unknown records. As a result, the recovery of vehicle identities is cast as an entity alignment task on a THIN. The proposed method integrates the vehicle group entities and context relations into the THIN for capturing the spatiotemporal relationships in vehicle travel and adopts a holographic embeddings model for better fitting the network structure. Empirically, we test it with a real ANPR dataset collected from Xuancheng, China, which has a densely-distributed camera network. The experiments demonstrate the effectiveness of the proposed graph structure under different missing rates. Further, we compare it with other embedding methods and the results support the superiority of holographic embeddings.

Artificial intelligence-based spatio-temporal vision sensors: applications and prospects

“…Xia et al (Xia et al, 2023) employed DAVIS346 to record the pulse flow, picture, and steering wheel angle of various illumination conditions. Using DAVIS240 and colour DAVIS346 respectively, Chen et al (Chen and he, 2020) built the DVS-Intensity and CED datasets for visual scene reconstruction. Zhang et al created the PKU-Spike-High-Speed dataset by using Vidar to record high-speed visual sceneries and moving objects.…”

A Higher-Order Motif-Based Spatiotemporal Graph Imputation Approach for Transportation Networks

“…As some graph-based methods consider strong relations of data structures [36,37], it is feasible of capturing global information to improve imputation performance. Chen and He proposed a heterogeneous graph embedding framework, which constructed a travel heterogeneous information network to find the best matched vehicles for the missing records [38]. By incorporating the spectral graph convolution operation, Cui et al developed the graph Markov network to handle missing values for short-term traffic forecasting [39].…”