Vehicle Re-identification in Context

© 2019, Springer Nature Switzerland AG. Existing vehicle re-identification (re-id) evaluation benchmarks consider strongly artificial test scenarios by assuming the availability of high quality images and fine-grained appearance at an almost constant image scale, reminiscent to images required for Automatic Number Plate Recognition, e.g. VeRi-776. Such assumptions are often invalid in realistic vehicle re-id scenarios where arbitrarily changing image resolutions (scales) are the norm. This makes the existing vehicle re-id benchmarks limited for testing the true performance of a re-id method. In this work, we introduce a more realistic and challenging vehicle re-id benchmark, called Vehicle Re-Identification in Context (VRIC). In contrast to existing vehicle re-id datasets, VRIC is uniquely characterised by vehicle images subject to more realistic and unconstrained variations in resolution (scale), motion blur, illumination, occlusion, and viewpoint. It contains 60,430 images of 5,622 vehicle identities captured by 60 different cameras at heterogeneous road traffic scenes in both day-time and night-time. Given the nature of this new benchmark, we further investigate a multi-scale matching approach to vehicle re-id by learning more discriminative feature representations from multi-resolution images. Extensive evaluations show that the proposed multi-scale method outperforms the state-of-the-art vehicle re-id methods on three benchmark datasets: VehicleID, VeRi-776, and VRIC (Available at http://qmul-vric.github.io )

Methods of the Vehicle Re-identification

Most of researchers use the vehicle re-identification based on classification. This always requires an update with the new vehicle models in the market. In this paper, two types of vehicle re-identification will be presented. First, the standard method, which needs an image from the search vehicle. VRIC and VehicleID data set are suitable for training this module. It will be explained in detail how to improve the performance of this method using a trained network, which is designed for the classification. The second method takes as input a representative image of the search vehicle with similar make/model, released year and colour. It is very useful when an image from the search vehicle is not available. It produces as output a shape and a colour features. This could be used by the matching across a database to re-identify vehicles, which look similar to the search vehicle. To get a robust module for the re-identification, a fine-grained classification has been trained, which its class consists of four elements: the make of a vehicle refers to the vehicle's manufacturer, e.g. Mercedes-Benz, the model of a vehicle refers to type of model within that manufacturer's portfolio, e.g. C Class, the year refers to the iteration of the model, which may receive progressive alterations and upgrades by its manufacturer and the perspective of the vehicle. Thus, all four elements describe the vehicle at increasing degree of specificity. The aim of the vehicle shape classification is to classify the combination of these four elements. The colour classification has been separately trained. The results of vehicle re-identification will be shown. Using a developed tool, the re-identification of vehicles on video images and on controlled data set will be demonstrated. This work was partially funded under the grant.Comment: Proceedings of the 2020 Intelligent Systems Conference (IntelliSys) Volume 1-3, 3-4 Sep. 2020 Amsterda

Multi-task mutual learning for vehicle re-identification

Vehicle re-identification (Re-ID) aims to search a specific vehicle instance across non-overlapping camera views. The main challenge of vehicle Re-ID is that the visual appearance of vehicles may drastically changes according to diverse viewpoints and illumination. Most existing vehicle Re-ID models cannot make full use of various complementary vehicle information, e.g. vehicle type and orientation. In this paper, we propose a novel Multi-Task Mutual Learning (MTML) deep model to learn discriminative features simultaneously from multiple branches. Specifically, we design a consensus learning loss function by fusing features from the final convolutional feature maps from all branches. Extensive comparative evaluations demonstrate the effectiveness of our proposed MTML method in comparison to the state-of-the-art vehicle Re-ID techniques on a large-scale benchmark dataset, VeRi-776. We also yield competitive performance on the NVIDIA 2019 AI City Challenge Track 2

Partition and Reunion: A Two-Branch Neural Network for Vehicle Re-identification

International audienceThe smart city vision raises the prospect that cities will become more intelligent in various fields, such as more sustainable environment and a better quality of life for residents. As a key component of smart cities, intelligent transportation system highlights the importance of vehicle re-identification (Re-ID). However, as compared to the rapid progress on person Re-ID, vehicle Re-ID advances at a relatively slow pace. Some previous state-of-the-art approaches strongly rely on extra annotation, like attributes (e.g., vehicle color and type) and key-points (e.g., wheels and lamps). Recent work on person Re-ID shows that extracting more local features can achieve a better performance without considering extra annotation. In this paper, we propose an end-to-end trainable two-branch Partition and Reunion Network (PRN) for the challenging vehicle Re-ID task. Utilizing only identity labels, our proposed method outperforms existing state-of-the-art methods on four vehicle Re-ID benchmark datasets, including VeRi-776, Vehi-cleID, VRIC and CityFlow-ReID by a large margin

Deep Representation Learning for Vehicle Re-Identification

With the widespread use of surveillance cameras in cities and on motorways, computer vision based intelligent systems are becoming a standard in the industry. Vehicle related problems such as Automatic License Plate Recognition have been addressed by computer vision systems, albeit in controlled settings (e.g.cameras installed at toll gates). Due to the freely available research data becoming available in the last few years, surveillance footage analysis for vehicle related problems are being studied with a computer vision focus. In this thesis, vision-based approaches for the problem of vehicle re-identification are investigated and original approaches are presented for various challenges of the problem. Computer vision based systems have advanced considerably in the last decade due to rapid improvements in machine learning with the advent of deep learning and convolutional neural networks (CNNs). At the core of the paradigm shift that has arrived with deep learning in machine learning is feature learning by multiple stacked neural network layers. Compared to traditional machine learning methods that utilise hand-crafted feature extraction and shallow model learning, deep neural networks can learn hierarchical feature representations as input data transform from low-level to high-level representation through consecutive neural network layers. Furthermore, machine learning tasks are trained in an end-to-end fashion that integrates feature extraction and machine learning methods into a combined framework using neural networks. This thesis focuses on visual feature learning with deep convolutional neural networks for the vehicle re-identification problem. The problem of re-identification has attracted attention from the computer vision community, especially for the person re-identification domain, whereas vehicle re-identification is relatively understudied. Re-identification is the problem of matching identities of subjects in images. The images come from non-overlapping viewing angles captured at varying locations, illuminations, etc. Compared to person re-identification, vehicle reidentification is particularly challenging as vehicles are manufactured to have the same visual appearance and shape that makes different instances visually indistinguishable. This thesis investigates solutions for the aforementioned challenges and makes the following contributions, improving accuracy and robustness of recent approaches. The contributions are the following: (1) Exploring the man-made nature of vehicles, that is, their hierarchical categories such as type (e.g.sedan, SUV) and model (e.g.Audi-2011-A4) and its usefulness in identity matching when identity pairwise labelling is not present (2) A new vehicle re-identification benchmark, Vehicle Re-Identification in Context (VRIC), is introduced to enable the design and evaluation of vehicle re-id methods to more closely reflect real-world application conditions compared to existing benchmarks. VRIC is uniquely characterised by unconstrained vehicle images in low resolution; from wide field of view traffic scene videos exhibiting variations of illumination, motion blur,and occlusion. (3) We evaluate the advantages of Multi-Scale Visual Representation (MSVR) in multi-scale cross-camera matching performance by training a multi-branch CNN model for vehicle re-identification enabled by the availability of low resolution images in VRIC. Experimental results indicate that this approach is useful in real-world settings where image resolution is low and varying across cameras. (4) With Multi-Task Mutual Learning (MTML) we propose a multi-modal learning representation e.g.using orientation as well as identity labels in training. We utilise deep convolutional neural networks with multiple branches to facilitate the learning of multi-modal and multi-scale deep features that increase re-identification performance, as well as orientation invariant feature learning