Vehicle Re-identification with the Space-Time Prior

Wu, Chih-Wei; Liu, Chih‐Ting; Chiang, Cheng-En; Tu, Wei-Chih; Chien, Shao-Yi

doi:10.1109/cvprw.2018.00024

Cited by 39 publications

(21 citation statements)

References 11 publications

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“…There are other methods based on representation learning. To address issues such as data labeling, visual domain mismatch between datasets and diverse appearance of the same vehicle, Wu et al [50] proposed a CNN-based vehicle re-identification system, the adaptive representation learning technique based on the space-time prior was used to automatically get positive and negative training samples from unlabeled testing videos. They trained a vehicle feature extractor in a multi-task learning manner and fine-tuned the feature extractor on the target domain so that the deep network could adapt to the visual domain of the testing videos.…”

Section: ) Vehicle Re-identification Methods Based On Representationmentioning

confidence: 99%

A Survey of Vehicle Re-Identification Based on Deep Learning

2019

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Vehicle re-identification is one of the core technologies of intelligent transportation systems, and it is crucial for the construction of smart cities. With the rapid development of deep learning, vehicle re-identification technologies have made significant progress in recent years. Therefore, making a comprehensive survey about the vehicle re-identification methods based on deep learning is quite indispensable. There are mainly five types of deep learning-based methods designed for vehicle re-identification, i.e. methods based on local features, methods based on representation learning, methods based on metric learning, methods based on unsupervised learning, and methods based on attention mechanism. The major contributions of our survey come from three aspects. First, we give a comprehensive review of the current five types of deep learning-based methods for vehicle re-identification, and we further compare them from characteristics, advantages, and disadvantages. Second, we sort out vehicle public datasets and compare them from multiple dimensions. Third, we further discuss the challenges and possible research directions of vehicle re-identification in the future based on our survey. INDEX TERMS Deep learning, intelligent transportation system, vehicle re-identification, vehicle public datasets.

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Section: ) Vehicle Re-identification Methods Based On Representationmentioning

confidence: 99%

A Survey of Vehicle Re-Identification Based on Deep Learning

2019

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“…Although BIR only uses visual information, our final approach also has a 17% and 12% increase in terms of mAP, as compared with Siamese-CNN+path-LSTM [20] and AFL+CNN [23], which also use spatio-temporal information. It proves that making full use of image information can improve accuracy.…”

Section: Effect Of Background Segmentationmentioning

confidence: 99%

Background Segmentation for Vehicle Re-identification

Ming-jie

Zhang

et al. 2019

Lecture Notes in Computer Science

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Vehicle re-identification (Re-ID) is very important in intelligent transportation and video surveillance. Prior works focus on extracting discriminative features from visual appearance of vehicles or using visual-spatio-temporal information. However, background interference in vehicle re-identification have not been explored. In the actual large-scale spatio-temporal scenes, the same vehicle usually appears in different backgrounds while different vehicles might appear in the same background, which will seriously affect the re-identification performance. To the best of our knowledge, this paper is the first to consider the background interference problem in vehicle re-identification. We construct a vehicle segmentation dataset and develop a vehicle Re-ID framework with a background interference removal (BIR) mechanism to improve the vehicle Re-ID performance as well as robustness against complex background in large-scale spatio-temporal scenes. Extensive experiments demonstrate the effectiveness of our proposed framework, with an average 9% gain on mAP over state-of-the-art vehicle Re-ID algorithms.

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“…The latest research in this field concerns multi-camera vehicle tracking and reidentification. Using learned high-level features for vehicle instance representation, vehicles can be tracked and reidentified on a city-wide scale (Nguyen et al, 2019;Wu et al, 2018). Likewise, large-scale tracking is achievable with an optimisation technique using spatiotemporal vehicle trajectories combined with visual feature recognition (Tan et al, 2019).…”

Section: Vehicle Trackingmentioning

confidence: 99%

Accurate Vehicle Speed Estimation From Monocular Camera Footage

Bell

Xiao

James

2020

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.

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Abstract. A workflow is devised in this paper by which vehicle speeds are estimated semi-automatically via fixed DSLR camera. Deep learning algorithm YOLOv2 was used for vehicle detection, while Simple Online Realtime Tracking (SORT) algorithm enabled for tracking of vehicles. Perspective projection and scale factor were dealt with by remotely mapping corresponding image and real-world coordinates through a homography. The ensuing transformation of camera footage to British National Grid Coordinate System, allowed for the derivation of real-world distances on the planar road surface, and subsequent simultaneous vehicle speed estimations. As monitoring took place in a heavily urbanised environment, where vehicles frequently change speed, estimations were determined consecutively between frames. Speed estimations were validated against a reference dataset containing precise trajectories from a GNSS and IMU equipped vehicle platform. Estimations achieved an average root mean square error and mean absolute percentage error of 0.625 m/s and 20.922 % respectively. The robustness of the method was tested in a real-world context and environmental conditions.

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Vehicle Re-identification with the Space-Time Prior

Cited by 39 publications

References 11 publications

A Survey of Vehicle Re-Identification Based on Deep Learning

A Survey of Vehicle Re-Identification Based on Deep Learning

Background Segmentation for Vehicle Re-identification

Accurate Vehicle Speed Estimation From Monocular Camera Footage

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