Vehicle Re-identification Problem Research Articles

Posture Calibration Based Cross-View & Hard-Sensitive Metric Learning for UAV-Based Vehicle Re-Identification

Machine vision based vehicle re-identification (ReID) plays an important role in some Intelligent Transportation Systems (ITS). Yet, most previous methods mainly focus on fixed surveillance cameras instead of Unmanned Aerial Vehicle (UAV). With high flexibility, the UAV-based vehicle ReID problem has some special challenges including complicated shooting angles, low discrimination of top-down features, and large variance in vehicle scales, etc. To overcome these challenges, we propose a novel structure for UAV-based vehicle ReID without license plates. Firstly, a triple-head segmentation net is proposed for segmenting UAV-captured vehicles under different heights and directions. Secondly, a posture calibration model is designed to uniform the vehicle postures based on the segmentation results, with the purpose of reducing the influence of different postures. Thirdly, the novel Cross-View & Hard-Sensitive Metric Learning (CHSML) method is proposed to train a ReID network with cross-view training constraint and hard sensitive principles; the mechanism of CHSML takes the cross-view samples of same ID as a training unit to learn the potential visual relationship in cross-view and builds a hard sensitive weight matrix to make learning more focus on hard samples, which improves the low ReID accuracy brought by cross-view or hard samples. Moreover, to facilitate the research of UAV-based vehicle ReID, a large-scale UAV-based vehicle ReID dataset called VeRi-UAV is released with 17 516 vehicles of 453 IDs. The experiments show that the proposed structure gains a better performance compared with the representative methods in the UAV-based vehicle ReID task.

Vehicle Re-Identification with Spatio-Temporal Model Leveraging by Pose View Embedding

Vehicle re-identification (Re-ID) research has intensified as numerous advancements have been made along with the rapid development of person Re-ID. In this paper, we tackle the vehicle Re-ID problem in open scenarios. This research differs from the early-stage studies that focused on a certain view, and it faces more challenges due to view variations, illumination changes, occlusions, etc. Inspired by the research of person Re-ID, we propose leveraging pose view to enhance the discrimination performance of visual features and utilizing keypoints to improve the accuracy of pose recognition. However, the visual appearance information is still limited by the changing surroundings and extremely similar appearances of vehicles. To the best of our knowledge, few methods have been aware of the spatio-temporal information to supplement visual appearance information, but they neglect the influence of the driving direction. Considering the peculiar characteristic of vehicle movements, we observe that vehicles’ poses on camera views indicating their directions are closely related to spatio-temporal cues. Consequently, we design a two-branch framework for vehicle Re-ID, including a Keypoint-based Pose Embedding Visual (KPEV) model and a Keypoint-based Pose-Guided Spatio-Temporal (KPGST) model. These models are integrated into the framework, and the results of KPEV and KPGST are fused based on a Bayesian network. Extensive experiments performed on the VeRi-776 and VehicleID datasets related to functional urban surveillance scenarios demonstrate the competitive performance of our proposed approach.

VARID: Viewpoint-Aware Re-IDentification of Vehicle Based on Triplet Loss

With the increasing prevalence of intelligent traffic control and monitoring, research on vehicle re-identification (Re-ID) draws substantial attention in recent years. Different from other cross-view searching tasks such as person Re-ID, the vehicle Re-ID problem is more challenging and unpredictable as viewpoint variations can greatly affect the appearance of vehicles. Existing studies mainly focus on extracting global features based on visual appearance to represent the identity of the target vehicle, while the impact of viewpoint variation is rarely considered. In this paper, we take the view information into account to boost vehicle Re-ID, and introduce latent view labels by clustering and incorporates view information into deep metric learning to tackle the challenge. We also develop a stricter center constraint to further improve the intra-class compactness of feature space. Moreover, we adopt an orthogonal regularization to increase the separability between different vehicles. VARID achieves 79.3% mAP on VeRi-776 and 88.5% mAP on VehicleID which surpasses state-of-the-arts a lot. More comprehensive experimental analyses and evaluations on four benchmarks demonstrate that the proposed method outperforms significantly state-of-the-arts methods.

Multi-Spectral Vehicle Re-Identification: A Challenge

Vehicle re-identification (Re-ID) is a crucial task in smart city and intelligent transportation, aiming to match vehicle images across non-overlapping surveillance camera views. Currently, most works focus on RGB-based vehicle Re-ID, which limits its capability of real-life applications in adverse environments such as dark environments and bad weathers. IR (Infrared) spectrum imaging offers complementary information to relieve the illumination issue in computer vision tasks. Furthermore, vehicle Re-ID suffers a big challenge of the diverse appearance with different views, such as trucks. In this work, we address the RGB and IR vehicle Re-ID problem and contribute a multi-spectral vehicle Re-ID benchmark named RGBN300, including RGB and NIR (Near Infrared) vehicle images of 300 identities from 8 camera views, giving in total 50125 RGB images and 50125 NIR images respectively. In addition, we have acquired additional TIR (Thermal Infrared) data for 100 vehicles from RGBN300 to form another dataset for three-spectral vehicle Re-ID. Furthermore, we propose a Heterogeneity-collaboration Aware Multi-stream convolutional Network (HAMNet) towards automatically fusing different spectrum features in an end-to-end learning framework. Comprehensive experiments on prevalent networks show that our HAMNet can effectively integrate multi-spectral data for robust vehicle Re-ID in day and night. Our work provides a benchmark dataset for RGB-NIR and RGB-NIR-TIR multi-spectral vehicle Re-ID and a baseline network for both research and industrial communities. The dataset and baseline codes are available at: https://github.com/ttaalle/multi-modal-vehicle-Re-ID.

Embedding Adversarial Learning for Vehicle Re-Identification.

The high similarities of different real-world vehicles and great diversities of the acquisition views pose grand challenges to vehicle re-identification (ReID), which traditionally maps the vehicle images into a high-dimensional embedding space for distance optimization, vehicle discrimination, and identification. To improve the discriminative capability and robustness of the ReID algorithm, we propose a novel end-to-end embedding adversarial learning network (EALN) that is capable of generating samples localized in the embedding space. Instead of selecting abundant hard negatives from the training set, which is extremely difficult if not impossible, with our embedding adversarial learning scheme, the automatically generated hard negative samples in the specified embedding space can greatly improve the capability of the network for discriminating similar vehicles. Moreover, the more challenging cross-view vehicle ReID problem, which requires the ReID algorithm to be robust with different query views, can also benefit from such a scheme based on the artificially generated cross-view samples. We demonstrate the promise of EALN through extensive experiments and show the effectiveness of hard negative and cross-view generation in facilitating vehicle ReID based on the comparisons with the state-of-the-art schemes.

Use of vehicle signature analysis and lexicographic optimization for vehicle reidentification on freeways

The vehicle reidentification problem is the task of matching a vehicle detected at one location with the same vehicle detected at another location from a feasible set of candidate vehicles detected at the other location. This paper formulates and solves the vehicle reidentification problem as a lexicographic optimization problem. Lexicographic optimization is a preemptive multi-objective formulation, and this lexicographic optimization formulation combines lexicographic goal programming, classification, and Bayesian analysis techniques. The solution of the vehicle reidentification problem has the potential to yield reliable section measures such as travel times and densities, and enables the measurement of partial dynamic origin/destination demands. Implementation of this approach using conventional surveillance infrastructure permits the development of new algorithms for ATMIS (Advanced Transportation Management and Information Systems). Freeway inductive loop data from SR-24 in Lafayette, California, demonstrates that robust results can be obtained under different traffic flow conditions.