Efficient vehicle detection and tracking strategy in aerial videos by employing morphological operations and feature points motion analysis

Gomaa, Ahmed; Abdelwahab, Moataz M.; Abo‐Zahhad, Mohammed

doi:10.1007/s11042-020-09242-5

Cited by 33 publications

(17 citation statements)

References 33 publications

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“…The shape and size of the structuring element significantly affect the final result. In image processing, morphological operations aim to remove these imperfections by considering the image form and structure [ 57 , 58 , 59 ].…”

Section: Methodsmentioning

confidence: 99%

A Novel Image Processing Approach to Enhancement and Compression of X-ray Images

Pourasad

Cavallaro

2021

IJERPH

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At present, there is an increase in the capacity of data generated and stored in the medical area. Thus, for the efficient handling of these extensive data, the compression methods need to be re-explored by considering the algorithm’s complexity. To reduce the redundancy of the contents of the image, thus increasing the ability to store or transfer information in optimal form, an image processing approach needs to be considered. So, in this study, two compression techniques, namely lossless compression and lossy compression, were applied for image compression, which preserves the image quality. Moreover, some enhancing techniques to increase the quality of a compressed image were employed. These methods were investigated, and several comparison results are demonstrated. Finally, the performance metrics were extracted and analyzed based on state-of-the-art methods. PSNR, MSE, and SSIM are three performance metrics that were used for the sample medical images. Detailed analysis of the measurement metrics demonstrates better efficiency than the other image processing techniques. This study helps to better understand these strategies and assists researchers in selecting a more appropriate technique for a given use case.

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Section: Methodsmentioning

confidence: 99%

A Novel Image Processing Approach to Enhancement and Compression of X-ray Images

Pourasad

Cavallaro

2021

IJERPH

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“…In [24], multi-criterion factors were analyzed to minimize life cycle cost. Yet another robust real-time approach utilizing top-hat and bottom-hat transformation using morphological operation was proposed in [25]. Structural Kalman filter was applied in [26] for enhancing detection and vehicle tracking to a greater extent.…”

Section: Related Workmentioning

confidence: 99%

Secured Vehicle Life Cycle Tracking Using Blockchain and Smart Contract

Bragadeesh¹,

Umamakeswari²

2022

Computer Systems Science and Engineering

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Life Cycle Tracking (LCT) involves continuous monitoring and analysis of various activities associated with a vehicle. The crucial factor in the LCT is to ensure the validity of gathered data as numerous supply chain phases are involved and the data is assessed by multiple stakeholders. Frauds and swindling activities can be prevented if the history of the vehicles is made available to the interested parties. Blockchain provides a way of enforcing trustworthiness to the supply chain participants and the data associated with the various actions performed. Machine learning techniques when combined decentralized nature of blockchains can be used to develop a robust Vehicle LCT model. In the proposed work, Harmonic Optimized Gradient Descent and Łukasiewicz Fuzzy (HOGD-LF) Vehicle Life Cycle Tracking in Cloud Environment is proposed and it involves three stages. First, the Progressive Harmonic Optimized User Registration and Authentication model is designed for computationally efficient registration and authentication. Next, for the authentic user, the Gradient Descent Blockchain-based SVM Data Encryption model is designed with minimum CPU utilization. Finally, Łukasiewicz Fuzzy Smart Contract Verification is performed with encrypted data to ensure accurate and precise fraudulent activity deduction. The experimental analysis shows that the proposed method achieves significant performance in terms of life cycle's prediction time, overhead, and accuracy for a different number of users.

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“…Lin et al [31] replaced temporal convolutions with a novel temporal shift module (TSM) over feature channels and enable 2-D convolutions to handle temporal information. Inspired by studies [32][33][34][35] of leveraging the appearance and motion representations to improve the performance of the deep learning model, the proposed approach in this work builds upon the success of the 2-D CNN and TSM for spatio-temporal feature extraction. Instead of directly taking advantage of the extract features, we additionally represent a novel STAG and a Similarity Graph based on the appearance and motion features, which enables the model to learn discriminative representation along the video with long-range spatial and temporal dependencies.…”

Section: Video Representationsmentioning

confidence: 99%

Exploring the Spatio‐Temporal Aware Graph for video captioning

Xue

Zhou

2022

IET Computer Vision

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Describing video content automatically in natural language sentences is a long‐standing challenge in computer vision. Although existing methods that capture relational information among objects have made significant strides in the past years, the detailed geometrical and temporal information of objects remains to be further explored. To address this problem, a novel Spatio‐Temporal Aware Graph is proposed to capture more elaborate visual representations, which are able to exploit the detailed spatio‐temporal clues of the extracted object features. By performing graph‐structured aggregation, the proposed model is capable of capturing not only the interactions among objects but also the detailed spatio‐temporal relations. Meanwhile, a Frame Similarity Graph is constructed on frame features to learn comprehensive representations, which aim to extract the global information that the object feature lacks. Moreover, to capture rich video semantics from different perspectives, multiple video representations, that is appearance and motion information, are utilised to learn discriminative representations. Experiments on the prevalent benchmarks: Microsoft Video Description Corpus and Microsoft Research Video to Text demonstrate that the proposed approach achieves state‐of‐the‐art performance in several widely used evaluation metrics: BLEU‐4, METEOR, ROUGE, and CIDEr.

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Efficient vehicle detection and tracking strategy in aerial videos by employing morphological operations and feature points motion analysis

Cited by 33 publications

References 33 publications

A Novel Image Processing Approach to Enhancement and Compression of X-ray Images

A Novel Image Processing Approach to Enhancement and Compression of X-ray Images

Secured Vehicle Life Cycle Tracking Using Blockchain and Smart Contract

Exploring the Spatio‐Temporal Aware Graph for video captioning

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