Car make and model recognition under limited lighting conditions at night

Boonsim, Noppakun; Prakoonwit, Simant

doi:10.1007/s10044-016-0559-6

Cited by 32 publications

(19 citation statements)

References 35 publications

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“…Liu et al [45] use deep relative distance trained on a vehicle re-identification task and propose training the neural net with Coupled Clusters Loss instead of triplet loss. Boonsim et al [46] propose a method for fine-grained recognition of vehicles at night. The authors use relative position and shape of features visible at night (e.g.…”

Section: B Fine-grained Recognition Of Vehiclesmentioning

confidence: 99%

BoxCars: Improving Fine-Grained Recognition of Vehicles Using 3-D Bounding Boxes in Traffic Surveillance

Sochor

Špaňhel

Herout

2019

IEEE Trans. Intell. Transport. Syst.

119

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In this paper, we focus on fine-grained recognition of vehicles mainly in traffic surveillance applications. We propose an approach that is orthogonal to recent advancements in finegrained recognition (automatic part discovery, bilinear pooling). Also, in contrast to other methods focused on fine-grained recognition of vehicles, we do not limit ourselves to a frontal/rear viewpoint, but allow the vehicles to be seen from any viewpoint. Our approach is based on 3D bounding boxes built around the vehicles. The bounding box can be automatically constructed from traffic surveillance data. For scenarios where it is not possible to use precise construction, we propose a method for an estimation of the 3D bounding box. The 3D bounding box is used to normalize the image viewpoint by "unpacking" the image into a plane. We also propose to randomly alter the color of the image and add a rectangle with random noise to a random position in the image during the training of Convolutional Neural Networks. We have collected a large fine-grained vehicle dataset BoxCars116k, with 116k images of vehicles from various viewpoints taken by numerous surveillance cameras. We performed a number of experiments which show that our proposed method significantly improves CNN classification accuracy (the accuracy is increased by up to 12 percentage points and the error is reduced by up to 50 % compared to CNNs without the proposed modifications). We also show that our method outperforms stateof-the-art methods for fine-grained recognition.

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Section: B Fine-grained Recognition Of Vehiclesmentioning

confidence: 99%

BoxCars: Improving Fine-Grained Recognition of Vehicles Using 3-D Bounding Boxes in Traffic Surveillance

Sochor

Špaňhel

Herout

2019

IEEE Trans. Intell. Transport. Syst.

119

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“…They evaluate two dictionary building approaches; single dictionary and modular dictionary and report an accuracy rate of 94.5% with a processing speed of 7.4 images per second. Noppakun Boonsim and Simant Prakoonwit propose a one-class classifier-based approach under limited lighting [19]. The proposed approach uses one-class SVM, decision tree, and K-Mean Nearest Neighbor (KNN) for classification and a majority vote of three is used for final prediction.…”

Section: Vehicle Make and Model Recognitionmentioning

confidence: 99%

Real-Time Vehicle Make and Model Recognition System

Manzoor

Morgan

Bais

2019

MAKE

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A Vehicle Make and Model Recognition (VMMR) system can provide great value in terms of vehicle monitoring and identification based on vehicle appearance in addition to the vehicles’ attached license plate typical recognition. A real-time VMMR system is an important component of many applications such as automatic vehicle surveillance, traffic management, driver assistance systems, traffic behavior analysis, and traffic monitoring, etc. A VMMR system has a unique set of challenges and issues. Few of the challenges are image acquisition, variations in illuminations and weather, occlusions, shadows, reflections, large variety of vehicles, inter-class and intra-class similarities, addition/deletion of vehicles’ models over time, etc. In this work, we present a unique and robust real-time VMMR system which can handle the challenges described above and recognize vehicles with high accuracy. We extract image features from vehicle images and create feature vectors to represent the dataset. We use two classification algorithms, Random Forest (RF) and Support Vector Machine (SVM), in our work. We use a realistic dataset to test and evaluate the proposed VMMR system. The vehicles’ images in the dataset reflect real-world situations. The proposed VMMR system recognizes vehicles on the basis of make, model, and generation (manufacturing years) while the existing VMMR systems can only identify the make and model. Comparison with existing VMMR research demonstrates superior performance of the proposed system in terms of recognition accuracy and processing speed.

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“…Lights relative positions are determined using morphological operators, then the distance from edges of ROI and lights to the centre of ROI are calculated and used as the feature vector. The authors in [17] utilised a combination of salient geographical and shape features of taillight and license plate to cope with limited lighting conditions at night. They employed SVM, decision tree and k‐NN using a majority‐voting scheme.…”

Section: Related Workmentioning

confidence: 99%