Learning latent features with local channel drop network for vehicle re-identification

Fu, Xianping; Peng, Jinjia; Jiang, Guangqi; Wang, Huibing

doi:10.1016/j.engappai.2021.104540

Cited by 16 publications

(3 citation statements)

References 22 publications

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“…There are also some methods that focus on the relationship modeling between samples and optimize the results of vehicle re-identification by learning the relationship distribution of vehicle images [22]. Data enhancement is also a framework to augment the diversity of the samples, so as to cover the data distribution of the vehicle images.…”

Section: Supervised Methodsmentioning

confidence: 99%

Focus on the Visible Regions: Semantic-Guided Alignment Model for Occluded Person Re-Identification

Yang

Wang

Fang

et al. 2020

Sensors

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The occlusion problem is very common in pedestrian retrieval scenarios. When persons are occluded by various obstacles, the noise caused by the occluded area greatly affects the retrieval results. However, many previous pedestrian re-identification (Re-ID) methods ignore this problem. To solve it, we propose a semantic-guided alignment model that uses image semantic information to separate useful information from occlusion noise. In the image preprocessing phase, we use a human semantic parsing network to generate probability maps. These maps show which regions of images are occluded, and the model automatically crops images to preserve the visible parts. In the construction phase, we fuse the probability maps with the global features of the image, and semantic information guides the model to focus on visible human regions and extract local features. During the matching process, we propose a measurement strategy that only calculates the distance of public areas (visible human areas on both images) between images, thereby suppressing the spatial misalignment caused by non-public areas. Experimental results on a series of public datasets confirm that our method outperforms previous occluded Re-ID methods, and it achieves top performance in the holistic Re-ID problem.

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Section: Supervised Methodsmentioning

confidence: 99%

Focus on the Visible Regions: Semantic-Guided Alignment Model for Occluded Person Re-Identification

Yang

Wang

Fang

et al. 2020

Sensors

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“…It can be mainly divided into supervised, metric and unsupervised learning methods. Methods based on supervised learning are further divided into methods based on global features [18,19], local features [20,21] and attention mechanism [22]. The goal of metric learning is to learn a mapping from the original features to the embedding space, such that the objects of the same category are close in the embedding space, and the distance between different categories is far away.…”

Section: Vehicle Re-identificationmentioning

confidence: 99%

Multi-Adjacent Camera-Based Dangerous Driving Trajectory Recognition for Ultra-Long Highways

Zhao,

Fu,

Yang

et al. 2024

Applied Sciences

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Fast detection of the trajectory is the key point to improve the further emergency proposal. Especially for ultra-long highway, prompt detection is labor-intensive. However, automatic detection relies on the accuracy and speed of vehicle detection, and tracking. In multi-camera surveillance system for ultra-long highways, it is often difficult to capture the same vehicle without intervals, which makes vehicle re-recognition crucial as well. In this paper, we present a framework that includes vehicle detection and tracking using improved DeepSORT, vehicle re-identification, feature extraction based on trajectory rules, and behavior recognition based on trajectory analysis. In particular, we design a network architecture based on DeepSORT with YOLOv5s to address the need for real-time vehicle detection and tracking in real-world traffic management. We further design an attribute recognition module to generate matching individuality attributes for vehicles to improve vehicle re-identification performance under multiple neighboring cameras. Besides, the use of bidirectional LSTM improves the accuracy of trajectory prediction, demonstrating its robustness to noise and fluctuations. The proposed model has a high advantage from the cumulative matching characteristic (CMC) curve shown and even improves above 15.38% compared to other state-of-the-art methods. The model developed on the local highway vehicle dataset is comprehensively evaluated, including abnormal trajectory recognition, lane change detection, and speed anomaly recognition. Experimental results demonstrate the effectiveness of the proposed method in accurately identifying various vehicle behaviors, including lane changes, stops, and even other dangerous driving behavior.

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“…It solves the problem of learning and recognising model information by learning local and global features of the vehicle. Fu et al [126] utilized local attention to facilitate the learning of local attentional features for vehicle re-ID. Liu et al [127] designed a vehicle information module.…”

Section: Vehicle Re-identification Based On Local Featurementioning

confidence: 99%

Multi-Object Multi-Camera Tracking Based on Deep Learning for Intelligent Transportation: A Review

Fei

Han

2023

Sensors

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Multi-Objective Multi-Camera Tracking (MOMCT) is aimed at locating and identifying multiple objects from video captured by multiple cameras. With the advancement of technology in recent years, it has received a lot of attention from researchers in applications such as intelligent transportation, public safety and self-driving driving technology. As a result, a large number of excellent research results have emerged in the field of MOMCT. To facilitate the rapid development of intelligent transportation, researchers need to keep abreast of the latest research and current challenges in related field. Therefore, this paper provide a comprehensive review of multi-object multi-camera tracking based on deep learning for intelligent transportation. Specifically, we first introduce the main object detectors for MOMCT in detail. Secondly, we give an in-depth analysis of deep learning based MOMCT and evaluate advanced methods through visualisation. Thirdly, we summarize the popular benchmark data sets and metrics to provide quantitative and comprehensive comparisons. Finally, we point out the challenges faced by MOMCT in intelligent transportation and present practical suggestions for the future direction.

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Learning latent features with local channel drop network for vehicle re-identification

Cited by 16 publications

References 22 publications

Focus on the Visible Regions: Semantic-Guided Alignment Model for Occluded Person Re-Identification

Focus on the Visible Regions: Semantic-Guided Alignment Model for Occluded Person Re-Identification

Multi-Adjacent Camera-Based Dangerous Driving Trajectory Recognition for Ultra-Long Highways

Multi-Object Multi-Camera Tracking Based on Deep Learning for Intelligent Transportation: A Review

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