Video-based Machine Learning System for Commodity Classification

He, Pan; Wu, Aotian; Huang, Xiaohui; Rangarajan, Anand; Ranka, Sanjay

doi:10.5220/0009393700002550

Proceedings of the 6th International Conference on Vehicle Technology and Intelligent Transport Systems 2020

DOI: 10.5220/0009393700002550

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Video-based Machine Learning System for Commodity Classification

Pan He

Aotian Wu

Xiaohui Huang

et al.

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Machine Learning-Based Highway Truck Commodity Classification Using Logo Data

Huang

et al. 2022

Applied Sciences

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In this paper, we propose a novel approach to commodity classification from surveillance videos by utilizing logo data on trucks. Broadly, most logos can be classified as predominantly text or predominantly images. For the former, we leverage state-of-the-art deep-learning-based text recognition algorithms on images. For the latter, we develop a two-stage image retrieval algorithm consisting of a universal logo detection stage that outputs all potential logo positions, followed by a logo recognition stage designed to incorporate advanced image representations. We develop an integrated approach to combine predictions from both the text-based and image-based solutions, which can help determine the commodity type that is potentially being hauled by trucks. We evaluated these models on videos collected in collaboration with the state transportation entity and achieved promising performance. This, along with prior work on trailer classification, can be effectively used for automatically deriving commodity types for trucks moving on highways.

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Machine Learning-Based Highway Truck Commodity Classification Using Logo Data

Huang

et al. 2022

Applied Sciences

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Video-based Machine Learning System for Commodity Classification

Cited by 1 publication

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Machine Learning-Based Highway Truck Commodity Classification Using Logo Data

Machine Learning-Based Highway Truck Commodity Classification Using Logo Data

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