Detecting Moving Vehicles from Satellite-Based Videos by Tracklet Feature Classification

Chen, Renxi; Ferreira, Vagner G.; Li, Xinhui

doi:10.3390/rs15010034

Cited by 3 publications

(2 citation statements)

References 22 publications

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“…Loss curve of VDTC-CEOADL algorithm on ISPRS Potsdam datasetThe accu_y inspection of the VDTC-CEOADL technique is compared with recent DL methods in Table3and Figure. 9[25,26]. The results indicate that the LeNet and VGG-19 models produced poor results with lower accu_y values of 96.56% and 97.06% respectively.…”

mentioning

confidence: 95%

Vehicle Detection and Classification using Optimal Deep Learning on High-Resolution Remote Sensing Imagery for Urban Traffic Monitoring

Alotaibi,

Nagappan,

et al. 2023

Preprint

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Remote sensing images (RSI), such as aerial or satellite images, produce a large-scale view of the Earth's surface, getting them used to track and monitor vehicles from several settings like border control, disaster response, and urban traffic surveillance. Vehicle detection and classification utilizing RSIs is a vital application of computer vision and image processing. It contains locating and identifying vehicles from the image. It is done using many approaches having object detection approaches, namely YOLO, Faster R-CNN, or SSD, that utilize deep learning (DL) for locating and identifying the image. Also, the vehicle classification from RSIs contains classifying them dependent upon their variety, like trucks, motorcycles, cars, or buses utilizing machine learning (ML) techniques. This article designed and develop an automated vehicle type detection and classification using a chaotic equilibrium optimization algorithm with deep learning (VDTC-CEOADL) on high resolution RSIs. The presented VDTC-CEOADL technique examines the high-quality RSIs for the accurate detection and classification of vehicles. The VDTC-CEOADL technique employs a YOLO-HR object detector with Residual Network as a backbone model to accomplish this. In addition, CEOA based hyperparameter optimizer is de-signed for the parameter tuning of the ResNet model. For the vehicle classification process, the VDTC-CEOADL technique exploits the attention based long short term memory (ALSTM) mod-el. The performance validation of the VDTC-CEOADL technique is validated on high resolution RSI dataset, and the results portrayed the supremacy of the VDTC-CEOADL technique in terms of different measures.

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confidence: 95%

Vehicle Detection and Classification using Optimal Deep Learning on High-Resolution Remote Sensing Imagery for Urban Traffic Monitoring

Alotaibi,

Nagappan,

et al. 2023

Preprint

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“…With the latest improvements in machine learning (ML) approaches, we are now capable of achieving higher object detection rates in cluttered scenes [7]. ML is a subfield of artificial intelligence (AI) that focuses on the design of algorithms and statistical models, which allows computers to learn and make predictions without being explicitly programmed.…”

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confidence: 99%

Anomaly Detection in Pedestrian Walkways for Intelligent Transportation System Using Federated Learning and Harris Hawks Optimizer on Remote Sensing Images

et al. 2023

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Anomaly detection in pedestrian walkways is a vital research area that uses remote sensing, which helps to optimize pedestrian traffic and enhance flow to improve pedestrian safety in intelligent transportation systems (ITS). Engineers and researchers can formulate more potential techniques and tools with the power of computer vision (CV) and machine learning (ML) for mitigating potential safety hazards and identifying anomalies (i.e., vehicles) in pedestrian walkways. The real-world challenges of scenes and dynamics of environmental complexity cannot be handled by the conventional offline learning-based vehicle detection method and shallow approach. With recent advances in deep learning (DL) and ML areas, authors have found that the image detection issue ought to be devised as a two-class classification problem. Therefore, this study presents an Anomaly Detection in Pedestrian Walkways for Intelligent Transportation Systems using Federated Learning and Harris Hawks Optimizer (ADPW-FLHHO) algorithm on remote sensing images. The presented ADPW-FLHHO technique focuses on the identification and classification of anomalies, i.e., vehicles in the pedestrian walkways. To accomplish this, the ADPW-FLHHO technique uses the HybridNet model for feature vector generation. In addition, the HHO approach is implemented for the optimal hyperparameter tuning process. For anomaly detection, the ADPW-FLHHO technique uses a multi deep belief network (MDBN) model. The experimental results illustrated the promising performance of the ADPW-FLHHO technique over existing models with a maximum AUC score of 99.36%, 99.19%, and 98.90% on the University of California San Diego (UCSD) Ped1, UCSD Ped2, and avenue datasets, respectively. Therefore, the proposed model can be employed for accurate and automated anomaly detection in the ITS environment.

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Structured Analysis of Traffic Video based on Deep Learning Algorithm

Tang

2023

2023 Global Conference on Information Technologies and Communications (GCITC)

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Detecting Moving Vehicles from Satellite-Based Videos by Tracklet Feature Classification

Cited by 3 publications

References 22 publications

Vehicle Detection and Classification using Optimal Deep Learning on High-Resolution Remote Sensing Imagery for Urban Traffic Monitoring

Vehicle Detection and Classification using Optimal Deep Learning on High-Resolution Remote Sensing Imagery for Urban Traffic Monitoring

Anomaly Detection in Pedestrian Walkways for Intelligent Transportation System Using Federated Learning and Harris Hawks Optimizer on Remote Sensing Images

Structured Analysis of Traffic Video based on Deep Learning Algorithm

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