Deep Learning based Geometric Features for Effective Truck Selection and Classification from Highway Videos

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

doi:10.1109/itsc.2019.8917097

Cited by 8 publications

(3 citation statements)

References 15 publications

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“…Similarly to [5], YOLO was adopted for truck detection. Then, CNNs were used to extract features of the truck components, such as truck size, trailers, and wheels, followed by decision trees to classify the trucks into three groups [13]. This work was continued by further introducing three discriminating features (shape, texture, and semantic information) to better identify the trailer types [14].…”

Section: Introductionmentioning

confidence: 99%

Image-Based Vehicle Classification by Synergizing Features from Supervised and Self-Supervised Learning Paradigms

Yang

2023

Eng

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This paper introduces a novel approach to leveraging features learned from both supervised and self-supervised paradigms, to improve image classification tasks, specifically for vehicle classification. Two state-of-the-art self-supervised learning methods, DINO and data2vec, were evaluated and compared for their representation learning of vehicle images. The former contrasts local and global views while the latter uses masked prediction on multiple layered representations. In the latter case, supervised learning is employed to finetune a pretrained YOLOR object detector for detecting vehicle wheels, from which definitive wheel positional features are retrieved. The representations learned from these self-supervised learning methods were combined with the wheel positional features for the vehicle classification task. Particularly, a random wheel masking strategy was utilized to finetune the previously learned representations in harmony with the wheel positional features during the training of the classifier. Our experiments show that the data2vec-distilled representations, which are consistent with our wheel masking strategy, outperformed the DINO counterpart, resulting in a celebrated Top-1 classification accuracy of 97.2% for classifying the 13 vehicle classes defined by the Federal Highway Administration.

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Section: Introductionmentioning

confidence: 99%

Image-Based Vehicle Classification by Synergizing Features from Supervised and Self-Supervised Learning Paradigms

Yang

2023

Eng

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“…Truck-based freight transportation is expected to grow in the next decade according to ATA's freight forecast. In response to this, the research community has developed various classification models for trucks and trailers, relying on the input data collected from traffic sensors such as weigh-in-motion (WIM), inductive loop detectors (ILD), and cameras [5][6][7][8]. However, the major limitation is that they fail to reveal the carried cargo from the limited cues identified from trucks.…”

Section: Introductionmentioning

confidence: 99%

“…Prior work has successfully inferred the commodity type based on the trailer types, e.g., enclosed or tank, recognized from some truck images [6]. However, it fails to handle the majority of trucks with enclosed trailers.…”

Section: Introductionmentioning

confidence: 99%

Machine Learning-Based Highway Truck Commodity Classification Using Logo Data

Huang

et al. 2022

Applied Sciences

Self Cite

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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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Self-annotated Labelling and Training Data for Traffic Video Object Detection Using Machine Learning Techniques

Rahul Chiranjeevi,

Swamy,

Krishna Prasath

et al. 2024

IFIP Advances in Information and Communication Technology

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Deep Learning based Geometric Features for Effective Truck Selection and Classification from Highway Videos

Cited by 8 publications

References 15 publications

Image-Based Vehicle Classification by Synergizing Features from Supervised and Self-Supervised Learning Paradigms

Image-Based Vehicle Classification by Synergizing Features from Supervised and Self-Supervised Learning Paradigms

Machine Learning-Based Highway Truck Commodity Classification Using Logo Data

Self-annotated Labelling and Training Data for Traffic Video Object Detection Using Machine Learning Techniques

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