Artificial Intelligence for Text-Based Vehicle Search, Recognition, and Continuous Localization in Traffic Videos

Panetta, Karen; Kezebou, Landry; Oludare, Victor; Intriligator, James; Agaian, Sos S.

doi:10.3390/ai2040041

Cited by 7 publications

(4 citation statements)

References 37 publications

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“…Datasets. We use the following datasets: CompCars [54], Box-Cars116K [42], Cars196 [24], VehicleColors [36], and VeRi [29]. We also obtained our own dataset of vehicles labeled with color and type annotations using a web crawler on a variety of car-sale sites, called CrawledVehicles.…”

Section: Methodsmentioning

confidence: 99%

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Constructive Interpretability with CoLabel: Corroborative Integration, Complementary Features, and Collaborative Learning

Suprem¹,

Vaidya²,

Cherkadi³

et al. 2022

Preprint

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Machine learning models with explainable predictions are increasingly sought after, especially for real-world, critical applications that require bias detection and risk mitigation. Inherent interpretability, where a model is designed from the ground-up to provide prediction explanations, provide more faithful insights to model performance. In this paper, we present CoLabel, a constructively interpretable model that provides explanations rooted in the ground truth. We evaluate CoLabel for vehicle feature extraction, since these are crucial for re-id traffic monitoring and management, tracking, and vehicle make-model recognition (VMMR). Each task is a critical application that requires interpretable models. By construction, CoLabel provides VMMR along with a composite of interpretable features. These interpretable features are based on interpretable annotations of the ground truth labels. CoLabel first performs corroborative integration to join multiple datasets that each have a subset of desired interpretable annotations. Then Co-Label uses decomposable branches to extract interpretable features and fuses them together for final predictions. During feature fusion, CoLabel performs harmonization of the complementary branches so that VMMR features can be projected to the same semantic space for classification. We show that CoLabel achieves superior accuracy to the state-of-the-art black-box models. Further, CoLabel provides explanations that help in self-diagnosing and verifying predictions.

show abstract

Section: Methodsmentioning

confidence: 99%

“…CompCars [54] labels make, model and type. VehicleColors [36] labels only the colors. BoxCars116K [42] labels make and model.…”

Section: Corroborative Integrationmentioning

confidence: 99%

Constructive Interpretability with CoLabel: Corroborative Integration, Complementary Features, and Collaborative Learning

Suprem¹,

Vaidya²,

Cherkadi³

et al. 2022

Preprint

View full text Add to dashboard Cite

show abstract

“…Datasets. We use the following datasets: CompCars [36], Box-Cars116K [27], Cars196 [12], VColors [20], and VeRi [15]. We also obtained our own dataset of vehicles labeled with color and type annotations using a web crawler on a variety of car-sale sites, called CVehicles.…”

Section: Methodsmentioning

confidence: 99%

“…Cars196 [12] contains make and model attributes of vehicle images. VColors [20] contains color labels for vehicles. CompCars [36] is a fine-grained dataset with make, model, and type annotations.…”

Section: Introductionmentioning

confidence: 99%

ATEAM: Knowledge Integration from Federated Datasets for Vehicle Feature Extraction using Annotation Team of Experts

Suprem¹,

Singh²,

Cherkadi³

et al. 2022

Preprint

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The vehicle recognition area, including vehicle make-model recognition (VMMR), re-id, tracking, and parts-detection, has made significant progress in recent years, driven by several large-scale datasets for each task. These datasets are often non-overlapping, with different label schemas for each task: VMMR focuses on make and model, while re-id focuses on vehicle ID. It is promising to combine these datasets to take advantage of knowledge across datasets as well as increased training data; however, dataset integration is challenging due to the domain gap problem. This paper proposes ATEAM, an annotation team-of-experts to perform cross-dataset labeling and integration of disjoint annotation schemas. ATEAM uses diverse experts, each trained on datasets that contain an annotation schema, to transfer knowledge to datasets without that annotation. Using ATEAM, we integrated several common vehicle recognition datasets into a Knowledge Integrated Dataset (KID). We evaluate ATEAM and KID for vehicle recognition problems and show that our integrated dataset can help off-the-shelf models achieve excellent accuracy on VMMR and vehicle re-id with no changes to model architectures. We achieve mAP of 0.83 on VeRi, and accuracy of 0.97 on CompCars. We have released both the dataset and the ATEAM framework for public use.

show abstract

Constructive Interpretability with CoLabel: Corroborative Integration, Complementary Features, and Collaborative Learning

Suprem

Vaidya

Cherkadi

et al. 2022

2022 IEEE 4th International Conference on Cognitive Machine Intelligence (CogMI)

View full text Add to dashboard Cite

Artificial Intelligence for Text-Based Vehicle Search, Recognition, and Continuous Localization in Traffic Videos

Cited by 7 publications

References 37 publications

Constructive Interpretability with CoLabel: Corroborative Integration, Complementary Features, and Collaborative Learning

Constructive Interpretability with CoLabel: Corroborative Integration, Complementary Features, and Collaborative Learning

ATEAM: Knowledge Integration from Federated Datasets for Vehicle Feature Extraction using Annotation Team of Experts

Constructive Interpretability with CoLabel: Corroborative Integration, Complementary Features, and Collaborative Learning

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