Vehicle Re-identification Research Articles

Multi-View Spatial Attention Embedding for Vehicle Re-Identification

Vehicle Re-Identification (Re-ID) is a challenging vision task mainly because the appearance of a vehicle varies dramatically under different viewpoints. Moreover, different vehicles with the same model and color commonly show similar appearance, thus are hard to be distinguished. To alleviate negative effects of viewpoint variance, we design a multi-view branch network where each branch learns a viewpoint-specific feature without parameter sharing. Being able to focus on a limited range of viewpoints, this viewpoint-specific feature performs substantially better than the general feature learned by an uniform network. To further differentiate visually similar vehicles, we strengthen the discriminative power on their subtle local differences by introducing a spatial attention model into each feature learning branch. The multi-view feature learning and spatial attention learning compose our neural network architecture, which is trained end to end with the softmax loss and triplet loss, respectively. We evaluate our methods on two large vehicle Re-ID datasets, i.e., VehicleID and VeRi-776, respectively. Extensive experiments show that our methods achieve promising performance. For example, we achieve mAP accuracy of 76.78% and 72.53% on VehicleID and VeRi-776 dataset respectively, substantially better than current state-of-the art.

Cross domain knowledge learning with dual-branch adversarial network for vehicle re-identification

The widespread popularization of vehicles has facilitated all people’s life during the last decades. However, the emergence of a large number of vehicles poses the critical but challenging problem of vehicle re-identification (reID). Till now, for most vehicle reID algorithms, both the training and testing processes are conducted on the same annotated datasets under supervision. However, even a well-trained model will still cause fateful performance drop due to the severe domain bias between the trained dataset and the real-world scenes.To address this problem, this paper proposes a domain adaptation framework for vehicle reID (DAVR), which narrows the cross-domain bias by fully exploiting the labeled data from the source domain to adapt the target domain. DAVR develops an image-to-image translation network named Dual-branch Adversarial Network (DAN), which promotes the images from the source domain (well-labeled) to learn the style of the target domain (unlabeled). Specially, DAN doesn’t need any annotation and can preserve identity information from source domain before and after translation. Furthermore, the generated images are employed to train the vehicle reID model by a proposed attention-based feature learning network. Through the proposed framework, the well-trained reID model has better generalization ability for various scenes in real-world situations. Comprehensive experimental results have demonstrated that our proposed DAVR can achieve excellent performances on benchmark datasets VehicleID and VeRi-776.

Vehicle Re-Identification Using Distance-Based Global and Partial Multi-Regional Feature Learning

Vehicle re-identification supports cross-camera tracking and the location of specific vehicles in a smart city. The gallery images of vehicles are ranked based on the similarities in the appearance of objects to a vehicle query image. Previous work on vehicle re-identification has mainly focused on global or local analyses of predefined regions of vehicles to classify the vehicle images with a softmax loss function. On the one hand, separate global or predefined local regions of vehicles are often sensitive to perspective and occlusions. On the other hand, the embedding space supervised by the softmax loss function is not sufficiently compact for the object class. To solve these problems, we propose an end-to-end distance-based global and partial multi-regional deep network (DGPM) that combines multi-regional features to identify global and local differences. We exploit a three-branch architecture to learn the global and partial features from coarsely partitioned regions. A global similarity module is introduced to reduce the background information interference in the local branches. Unlike general classification, we design a distance-based classification layer that maintains consistency among criteria for similarity evaluation. Furthermore, we use spatiotemporal vehicle information to improve the vehicle re-identification results when the camera and shooting time are available. Systematic comparative evaluations performed on the large-scale VeRi and VehicleID datasets showed that our approach robustly achieved state-of-the-art performance. For instance, for the VeRi dataset, we achieve (79.39 + 2.78)% mAP and (96.19 + 2.26)% Rank-1 accuracy.

Learning an Evolutionary Embedding via Massive Knowledge Distillation

Knowledge distillation methods aim at transferring knowledge from a large powerful teacher network to a small compact student one. These methods often focus on close-set classification problems and matching features between teacher and student networks from a single sample. However, many real-world classification problems are open-set. This paper proposes an Evolutionary Embedding Learning (EEL) framework to learn a fast and accurate student network for open-set problems via massive knowledge distillation. First, we revisit the formulation of canonical knowledge distillation and make it suitable for the open-set problems with massive classes. Second, by introducing an angular constraint, a novel correlated embedding loss (CEL) is proposed to match embedding spaces between the teacher and student network from a global perspective. Lastly, we propose a simple yet effective paradigm towards a fast and accurate student network development for knowledge distillation. We show the possibility to implement an accelerated student network without sacrificing accuracy, compared with its teacher network. The experimental results are quite encouraging. EEL achieves better performance with other state-of-the-art methods for various large-scale open-set problems, including face recognition, vehicle re-identification and person re-identification.

Unsupervised domain adaptive re-identification: Theory and practice

We study the problem of unsupervised domain adaptive re-identification (re-ID) which is an active topic in computer vision but lacks a theoretical foundation. We first extend existing unsupervised domain adaptive classification theories to re-ID tasks. Concretely, we introduce some assumptions on the extracted feature space and then derive several loss functions guided by these assumptions. To optimize them, a novel self-training scheme for unsupervised domain adaptive re-ID tasks is proposed. It iteratively makes guesses for unlabeled target data based on an encoder and trains the encoder based on the guessed labels. Extensive experiments on unsupervised domain adaptive person re-ID and vehicle re-ID tasks with comparisons to the state-of-the-arts confirm the effectiveness of the proposed theories and self-training framework. Our code is available on https://github.com/LcDog/DomainAdaptiveReID.

Unifying Person and Vehicle Re-Identification

Person and vehicle re-identification (re-ID) are important challenges for the analysis of the burgeoning collection of urban surveillance videos. To efficiently evaluate such videos, which are populated with both vehicles and pedestrians, it would be preferable to have one unified framework with effective performance across both domains. Unfortunately, due to the contrasting composition of humans and vehicles, no architecture has yet been established that can adequately perform both tasks. We release a Person and Vehicle Unified Data Set (PVUD) comprising of both pedestrians and vehicles from popular existing re-ID data sets, in order to better model the data that we would expect to find in the real world. We exploit the generalisation ability of metric learning to propose a re-ID framework that can learn to re-identify humans and vehicles simultaneously. We design our network, MidTriNet, to harness the power of mid-level features to develop better representations for the re-ID tasks. We help the system to handle mixed data by appending unification terms with additional hard negative and hard positive mining to MidTriNet. We attain comparable accuracy training on PVUD to training on the comprising data sets separately, supporting the system's generalisation power. To further demonstrate the effectiveness of our framework, we also obtain results better than, or competitive with, the state-of-the-art on each of the Market-1501, CUHK03, VehicleID and VeRi data sets.

Vehicle re-identification using relative entropy representation and gated recurrent unit networks

Abstract Vehicle Re-Identification is a prevailing subject in the field of computer vision technologies. Major problem in vehicle re-id is re identifying the vehicle from different viewpoints with occlusion and low illumination. In this work, we have proposed a deep-learning framework which effectively processes the latent representation of the vehicle image instances. The key to our framework is GRUs, which is used to determine the relationship between the vehicle image instances over a period of time, without compromising the computational cost, which is also used to encode the data.

A Novel Soft Margin Loss Function for Deep Discriminative Embedding Learning

Deep embedding learning aims to learn discriminative feature representations through a deep convolutional neural network model. Commonly, such a model contains a network architecture and a loss function. The architecture is responsible for hierarchical feature extraction, while the loss function supervises the training procedure with the purpose of maximizing inter-class separability and intra-class compactness. By considering that loss function is crucial for the feature performance, in this article we propose a new loss function called soft margin loss (SML) based on a classification framework for deep embedding learning. Specifically, we first normalize the learned features and the classification weights to map them into the hypersphere. After that, we construct our loss with the difference between the maximum intra-class distance and minimum inter-class distance. By constraining the distance difference with a soft margin that is inherent in the proposed loss, both the inter-class discrepancy and intra-class compactness of learned features can be effectively improved. Finally, under the joint training with an improved softmax loss, the model can learn features with strong discriminability. Toy experiments on MNIST dataset are conducted to show the effectiveness of the proposed method. Additionally, experiments on re-identification tasks are also provided to demonstrate the superior performance of embedding learning. Specifically, 65.48% / 62.68% mAP on CUHK03 labeled / detected dataset (person re-id) and 74.36% mAP on VeRi-776 dataset (vehicle re-id) are achieved respectively.

A privacy-compliant approach to responsible dataset utilisation for vehicle re-identification

A privacy-compliant approach to responsible dataset utilisation for vehicle re-identification

A Strong and Efficient Baseline for Vehicle Re-Identification Using Deep Triplet Embedding

Abstract In this paper we tackle the problem of vehicle re-identification in a camera network utilizing triplet embeddings. Re-identification is the problem of matching appearances of objects across different cameras. With the proliferation of surveillance cameras enabling smart and safer cities, there is an ever-increasing need to re-identify vehicles across cameras. Typical challenges arising in smart city scenarios include variations of viewpoints, illumination and self occlusions. Most successful approaches for re-identification involve (deep) learning an embedding space such that the vehicles of same identities are projected closer to one another, compared to the vehicles representing different identities. Popular loss functions for learning an embedding (space) include contrastive or triplet loss. In this paper we provide an extensive evaluation of triplet loss applied to vehicle re-identification and demonstrate that using the recently proposed sampling approaches for mining informative data points outperform most of the existing state-of-the-art approaches for vehicle re-identification. Compared to most existing state-of-the-art approaches, our approach is simpler and more straightforward for training utilizing only identity-level annotations, along with one of the smallest published embedding dimensions for efficient inference. Furthermore in this work we introduce a formal evaluation of a triplet sampling variant (batch sample) into the re-identification literature. In addition to the conference version [24], this submission adds extensive experiments on new released datasets, cross domain evaluations and ablation studies.

Vehicle re-identification in tunnel scenes via synergistically cascade forests

Nowadays, numerous cameras have been equipped in tunnels for monitoring the tunnel safety, such as detecting fire, vehicle stopping, crashes, and so forth. Nevertheless, safety events in tunnels may occur in the blind zones not covered by the multi-camera monitoring systems. Therefore, this paper opens the challenging problem, tunnel vehicle re-identification (abbr. tunnel vehicle Re-ID), to make a between-camera speculation. Different from the open road scenes focused by existing vehicle Re-ID methods, tunnel vehicle Re-ID is more challenging because of poor light condition, low resolution, frequent occlusion, severe motion blur, high between-vehicle similarity, and so on. To be specific, we propose a synergistically cascade forests (SCF) model which aims to gradually construct the linking relation between vehicle samples with an increasing of alternative layers of random forest and extremely randomized forest. Through the modeling of SCF, we can restrict the influence of little inter-variation of different vehicle identities and large intra-variation of the same identities. This paper constructs a new and challenging tunnel vehicle dataset (Tunnel-VReID), consisting of 1000 pairs of tunnel vehicle images. Extensive experiments on our Tunnel-VReID demonstrate that the proposed method can outperform current state-of-the-art methods. Besides, in order to prove the adaptation ability of SCF, we also verify the superiority of SCF on a large-scale vehicle Re-ID dataset, named as VehicleID, collected in open road scenes.

Learning feature aggregation in temporal domain for re-identification

Person re-identification is a standard and established problem in the computer vision community. In recent years, vehicle re-identification is also getting more attention. In this paper, we focus on both these tasks and propose a method for aggregation of features in temporal domain as it is common to have multiple observations of the same object. The aggregation is based on weighting different elements of the feature vectors by different weights and it is trained in an end-to-end manner by a Siamese network. The experimental results show that our method outperforms other existing methods for feature aggregation in temporal domain on both vehicle and person re-identification tasks. Furthermore, to push research in vehicle re-identification further, we introduce a novel dataset CarsReId74k. The dataset is not limited to frontal/rear viewpoints. It contains 17,681 unique vehicles, 73,976 observed tracks, and 277,236 positive pairs. The dataset was captured by 66 cameras from various angles.

Visual Cognition–Inspired Multi-View Vehicle Re-Identification via Laplacian-Regularized Correlative Sparse Ranking

Vehicle re-identification has gradually gained attention and widespread applications. However, most of the existing methods learn the discriminative features for identities by single-feature channel only. It is worth noting that visual cognition of the human eyes is a multi-channel system which usually seeks a sparse representation. Therefore, integrating the multi-view information in sparse representation is a natural way to boost computer vision tasks in challenging scenarios. In this paper, we propose to mine multi-view deep features via Laplacian-regularized correlative sparse ranking for vehicle re-identification. Specifically, first, we employ multiple baseline networks to generate features. Then, we explore the feature correlation via enforcing the correlation term into the multi-view Laplacian sparse ranking framework. The original rankings are obtained by the reconstruction coefficients between the probe and gallery. Finally, we utilize a re-ranking technique to further boost performance. Experimental results on public benchmark VeRi-776 and VehicleID datasets demonstrate that our approach outperforms state-of-the-art approaches. The Laplacian-regularized correlative sparse ranking as a general framework can be used in any multi-view feature fusion and will obtain more competitive results.

Group-Group Loss Based Global-Regional Feature Learning for Vehicle Re-Identification.

Vehicle Re-Identification (Re-ID) is challenging because vehicles of the same model commonly show similar appearance. We tackle this challenge by proposing a Global-Regional Feature (GRF) that depicts extra local details to enhance discrimination power in addition to the global context. It is motivated by the observation that, vehicles of same color, maker, and model can be distinguished by their regional difference, e.g., the decorations on the windshields. To accelerate the GRF learning and promote its discrimination power, we propose a Group-Group Loss (GGL) to optimize the distance within and across vehicle image groups. Different from the siamese or triplet loss, GGL is directly computed on image groups rather than individual sample pairs or triplets. By avoiding traversing numerous sample combinations, GGL makes the model training easier and more efficient. Those two contributions highlight this work from previous methods on vehicle Re-ID task, which commonly learn global features with triplet loss or its variants. We evaluate our methods on two large-scale vehicle Re-ID datasets, i.e., VeRi and VehicleID. Experimental results show our methods achieve promising performance in comparison with recent works.

Orientation- and Scale-Invariant Multi-Vehicle Detection and Tracking from Unmanned Aerial Videos

Along with the advancement of light-weight sensing and processing technologies, unmanned aerial vehicles (UAVs) have recently become popular platforms for intelligent traffic monitoring and control. UAV-mounted cameras can capture traffic-flow videos from various perspectives providing a comprehensive insight into road conditions. To analyze the traffic flow from remotely captured videos, a reliable and accurate vehicle detection-and-tracking approach is required. In this paper, we propose a deep-learning framework for vehicle detection and tracking from UAV videos for monitoring traffic flow in complex road structures. This approach is designed to be invariant to significant orientation and scale variations in the videos. The detection procedure is performed by fine-tuning a state-of-the-art object detector, You Only Look Once (YOLOv3), using several custom-labeled traffic datasets. Vehicle tracking is conducted following a tracking-by-detection paradigm, where deep appearance features are used for vehicle re-identification, and Kalman filtering is used for motion estimation. The proposed methodology is tested on a variety of real videos collected by UAVs under various conditions, e.g., in late afternoons with long vehicle shadows, in dawn with vehicles lights being on, over roundabouts and interchange roads where vehicle directions change considerably, and from various viewpoints where vehicles’ appearance undergo substantial perspective distortions. The proposed tracking-by-detection approach performs efficiently at 11 frames per second on color videos of 2720p resolution. Experiments demonstrated that high detection accuracy could be achieved with an average F1-score of 92.1%. Besides, the tracking technique performs accurately, with an average multiple-object tracking accuracy (MOTA) of 81.3%. The proposed approach also addressed the shortcomings of the state-of-the-art in multi-object tracking regarding frequent identity switching, resulting in a total of only one identity switch over every 305 tracked vehicles.

Deep Quadruplet Appearance Learning for Vehicle Re-Identification

Vehicle re-identification (Re-ID) plays an important role in intelligent transportation systems. It usually suffers from various challenges encountered on the real-life environments, such as viewpoint variations, illumination changes, object occlusions, and other complicated scenarios. To effectively improve the vehicle Re-ID performance, a new method, called the deep quadruplet appearance learning (DQAL), is proposed in this paper. The novelty of the proposed DQAL lies on the consideration of the special difficulty in vehicle Re-ID that the vehicles with the same model and color but different identities (IDs) are highly similar to each other. For that, the proposed DQAL designs the concept of quadruplet and forms the quadruplets as the input, where each quadruplet is composed of the anchor (or target), positive, negative, and the specially considered high-similar (i.e., the same model and color but different IDs with respect to the anchor) vehicle samples. Then, the quadruplet network with the incorporation of the proposed quadruplet loss and softmax loss is developed to learn a more discriminative feature for vehicle Re-ID, especially discerning those difficult high-similar cases. Extensive experiments conducted on two commonly used datasets VeRi-776 and VehicleID have demonstrated that the proposed DQAL approach outperforms multiple recently reported vehicle Re-ID methods.

Vehicle Re-Identification Using Quadruple Directional Deep Learning Features

In order to resist the adverse effect of viewpoint variations for improving vehicle re-identification performance, we design quadruple directional deep learning networks to extract quadruple directional deep learning features (QD-DLF) of vehicle images. The quadruple directional deep learning networks are with similar overall architecture, including the same basic deep learning architecture but different directional feature pooling layers. Specifically, the same basic deep learning architecture is a shortly and densely connected convolutional neural network to extract basic feature maps of an input square vehicle image in the first stage. Then, the quadruple directional deep learning networks utilize different directional pooling layers, i.e., horizontal average pooling (HAP) layer, vertical average pooling (VAP) layer, diagonal average pooling (DAP) layer and anti-diagonal average pooling (AAP) layer, to compress the basic feature maps into horizontal, vertical, diagonal and anti-diagonal directional feature maps, respectively. Finally, these directional feature maps are spatially normalized and concatenated together as a quadruple directional deep learning feature for vehicle re-identification. Extensive experiments on both VeRi and VehicleID databases show that the proposed QD-DLF approach outperforms multiple state-of-the-art vehicle re-identification methods.

Front-End Smart Visual Sensing and Back-End Intelligent Analysis: A Unified Infrastructure for Economizing the Visual System of City Brain

The visual data, which are acquired from the ubiquitous visual sensors deployed in metropolitans, are of great value and paramount significance to enhance the effectiveness and pursue the future development of smart cities. In this paper, the essential building blocks of the unified visual data management and analysis infrastructure that serve as the foundation for the economical visual system in the city brain, are introduced to facilitate the utilization of the visual signal in the artificial intelligence era. In particular, we start by the discussion of the front-end smart visual sensing in the context of economical communication and service with the heterogeneous network, and the functionalities and necessities of compact visual feature and deep learning model representations are detailed. Subsequently, the utilities of the infrastructure are demonstrated through two intelligent applications at the back-end, including vehicle re-identification and person re-identification. The standardizations regarding compact feature and deep neural network representations, which are regarded as the key ingredients in this infrastructure and greatly facilitate the construction of the visual system in the city brain, are also discussed. Finally, we envision how the potential issues regarding the economical visual communications for future smart cities might be pragmatically approached within this unified infrastructure.

Joint horizontal and vertical deep learning feature for vehicle re-identification

Joint horizontal and vertical deep learning feature for vehicle re-identification

Learning multi-region features for vehicle re-identification with context-based ranking method

Vehicle re-identification is to identify a target vehicle in different cameras with non-overlapping views. It is a challenging task due to various viewpoints of vehicles, diversified illuminations and complicated environments. Most existing vehicle re-identification methods focus on learning global features, while neglecting the importance of local features. In this paper, we propose a Multi-Region Model (MRM) to learn powerful features for vehicle re-identification. In addition to extracting global region features, MRM also extracts features from a series of local regions. For each local region, instead of utilizing the rigid part to extract features directly, a Spatial Transformer Network (STN) based localization model is introduced to localize local regions which contain more distinctive visual cues. In order to further improve the performance of re-identification, we design a context-based ranking method which generates the ranking list by taking context and content into consideration to measure the similarity between neighbors. Experimental results clearly demonstrate that our method achieves excellent performance on both VehicleID dataset and VeRi-776 dataset.