Real-Time Vehicle Make and Model Recognition Based on a Bag of SURF Features

Siddiqui, Abdul Jabbar; Mammeri, Abdelhamid; Boukerche, Azzedine

doi:10.1109/tits.2016.2545640

Cited by 63 publications

(45 citation statements)

References 30 publications

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“…Boukerch et al presented a real-time VMMR system and evaluated it in [18]. SVM is used as single multiclass classifier and ensemble of the multi-class classifier.…”

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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“…Boukerch et al presented a real-time VMMR system and evaluated it in [18]. SVM is used as single multiclass classifier and ensemble of the multi-class classifier.…”

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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“…29 Thus, the feature-based methods, including point, line, contour, 30,31 and so on, are widely used. Some representative feature-based methods have been presented in recent research studies, such as SIFT, 12,32 SURF, 33 GLOH, 34 and BRISK. 35 The SIFT-based method outperforms other algorithms because of its invariant image rotation, scaling, and illumination variations.…”

Section: Natural Landmark Establishment and Recognitionmentioning

confidence: 99%

Hybrid visual natural landmark–based localization for indoor mobile robots

Zhang

Zhu

Wang

et al. 2018

International Journal of Advanced Robotic Systems

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Localization is a crucial part of autonomous moving for the indoor mobile robot. The natural features of the ceiling and surrounding environment can serve for position estimation. Based on these natural features, a hybrid visual natural landmarks-based localization method is proposed. We combine the landmarks-based positioning with ceiling-based visual odometry. During the visual odometry, the orientation is computed from the parallel features between the adjacent frames. The position is calculated from the corresponding point features in the two consecutive images using the perspective-n-point method. During the natural landmarks-based localization, the orientation filter is utilized to obtain the global orientation. Then, the feature points are determined by the Compute Unified Device Architecture-based scaleinvariant feature transform algorithm. Finally, the position is estimated based on the computed orientation and point features. Various experiments have been conducted to evaluate the effectiveness of the proposed method. The experimental results show that the proposed localization method outperforms other methods in accuracy and efficiency.

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“…The full vehicle extent is detected using frame differencing by Ren and Lan 2 or background subtraction by Prokaj and Medioni. 3 Siddiqui et al 4 and Petrović and Cootes 5 extended detections from a licenseplate detector. Wijnhoven and de With 6 proposed a histogram of oriented gradient (HOG) 7 to obtain contrast-invariant detection.…”

Section: Related Workmentioning

confidence: 99%

“…The semiautomatic gathering of the dataset is explained in Sec. 4 and a detailed evaluation of our system on the dataset is discussed in Sec. 5.…”

Section: Introductionmentioning

confidence: 99%

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Semiautomatic training and evaluation of a learning-based vehicle make and model recognition system

et al. 2018

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Abstract. We describe a system for vehicle make and model recognition (MMR) that automatically detects and classifies the make and model of a car from a live camera mounted above the highway. Vehicles are detected using a histogram of oriented gradient detector and then classified by a convolutional neural network (CNN) incorporating the frontal view of the car. We propose a semiautomatic data-selection approach for the vehicle detector and the classifier, by using an automatic number plate recognition engine to minimize human effort. The resulting classification has a top-1 accuracy of 97.3% for 500 vehicle models. This paper presents a more extensive in-depth evaluation. We evaluate the effect of occlusion and have found that the most informative vehicle region is the grill at the front. Recognition remains accurate when the left or right part of vehicles is occluded. The small fraction of misclassifications mainly originates from errors in the dataset, or from insufficient visual information for specific vehicle models. Comparison of state-of-the-art CNN architectures shows similar performance for the MMR problem, supporting our findings that the classification performance is dominated by the dataset quality.

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Real-Time Vehicle Make and Model Recognition Based on a Bag of SURF Features

Cited by 63 publications

References 30 publications

Real-Time Vehicle Make and Model Recognition System

Real-Time Vehicle Make and Model Recognition System

Hybrid visual natural landmark–based localization for indoor mobile robots

Semiautomatic training and evaluation of a learning-based vehicle make and model recognition system

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