Enhancing Vehicle State Recognition in Logistics Industrial Parks via Dynamic Hidden Markov Model

Abstract

Platform-based vehicle recognition is a critical task in logistics scenarios that facilitates the efficient management of resources. Although recent advances in the computer vision domain can be conveniently adopted to recognize the identities of vehicles and the occupations of platforms, the efficacy is significantly compromised by the severe interference and noise at the platforms of logistics industrial parks. This work tackles these difficulties through concentrating on the sequential characteristics of vehicles during arrival and departure. An innovative dynamic hidden Markov model (DHMM) is proposed to estimate the real sequence of vehicle states from the noisy observations. A dynamic Viterbi algorithm is also developed to solve the proposed DHMM method with high efficiency. The proposed method is evaluated against multiple baselines through experiments, where it can recognize the vehicle states with high accuracy and is demonstrated to significantly outperform the baselines when the interference is strong.