Smart and Multi-Way Attendance Tracking System Using an Image-Processing Technique

Muthumari, M.; Akash, V.; Charan, K. Prudhvi; Deepak, V.; Praveen, S. Phani

doi:10.1109/icssit53264.2022.9716349

Cited by 12 publications

(2 citation statements)

References 8 publications

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“…The accuracy of our detection model is calculated by IoU. As shown in equation (23), IoU is the intersection over the union of the model's predicted bounding box and ground truth, where A pb is the predicted bounding box; A gt is the ground truth; area () is the intersection shape of A pb and A .…”

Section: Datasetmentioning

confidence: 99%

Vehicle detection in varied weather conditions using enhanced deep YOLO with complex wavelet

Keerthi Kiran,

Dash,

Parida

2024

Eng. Res. Express

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Traffic congestion is prevalent in many major and medium-sized cities throughout different countries in contemporary society. In traffic images, various multi-sized vehicles are tightly clustered together and obstructed from one another. Identifying vehicles in such instances is crucial for urban traffic surveillance, safety monitoring, and legal concerns but it also presents major challenges. The remarkable detection accuracy and efficiency of deep learning-based systems have led to their recent and extensive use in vehicle identification. There are significant advanced YOLO models with different backbone architectures and frameworks developed for vehicle detection. Yet, the performance of YOLO variants is facing the challenges of handling false detection against occluded and densely sophisticated scenarios. The proposed model is developed to address such types of limitations, for example; dynamic illumination, noisy images, and scale sensitivity to improve the vehicle detection rate in different traffic scenarios and varying weather conditions. The proposed study employs an improved YOLOv4 to identify moving vehicles in different lighting conditions including daylight, cloudy, rainy, and night. For hybridization, three techniques are utilized such as the Multiscale Retinex, Dual tree complex wavelet transform (DTCWT), and Pulse Coupled Neural Networks (PCNN). The DTCWT is employed for multiscale decomposition and to denoise the complex high frequency subband information, then the denoised subbands are reconstructed into a denoised image. The Multiscale retinex is utilized to reduce the halo artifacts on high-contrast edges and maintain the balance with dynamic range compression and color reproduction. The synchronizing pulse burst property of PCNN is used to detect the isolated noisy pixels and modify the detected noisy pixels. From the results it is worth noting that the developed model surpasses state-of-the-art methods in sunny, night, cloudy, and rainy modes. The proposed method using the DTCWT technique can detect the vehicles with mAP of 91.095% and 35FPS.

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Section: Datasetmentioning

confidence: 99%

Vehicle detection in varied weather conditions using enhanced deep YOLO with complex wavelet

Keerthi Kiran,

Dash,

Parida

2024

Eng. Res. Express

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“…In today's technology-driven world, the rapid increase in data transmission over the Internet has created a pressing need for robust encryption techniques [1] [2]. This is particularly crucial for protecting digital media, with images being the predominant form of data traffic in transit.…”

Section: Introductionmentioning

confidence: 99%

A Novel Dual Confusion and Diffusion Approach for Grey Image Encryption using Multiple Chaotic Maps

Praveen,

Suntharam,

Ravi

et al. 2023

IJACSA

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With the exponential growth of the internet and social media, images have become a predominant form of information transmission, including confidential data. Ensuring the proper security of these images has become crucial in today's digital age. This research study proposes a unique strategy for solving this demand by presenting a dual confusion and diffusion technique for encrypting gray-scale pictures. This method is presented as an innovative means of meeting this need. To improve the effectiveness of the encryption process, the encryption method uses several chaotic maps, including the logistic map, the tent map, and the Lorenz attractor. Python is used for the implementation of the suggested approach. Furthermore, a thorough assessment of the encryption mechanism is carried out to determine its efficacy and resilience. By employing the combined strength of chaotic maps and dual confusion and diffusion techniques, the proposed method aims to provide a high level of security for confidential image transmission. The experimental results demonstrate the algorithm's effectiveness in terms of encryption speed, security, and resistance against common attacks. The encrypted images exhibit properties such as randomness, key sensitivity, and resilience against statistical analysis and differential attacks. Moreover, the proposed method maintains a reasonable computational efficiency, and it is compatible with real-time applications. This study makes a contribution to the growing area of picture encryption by presenting an original and effective encryption method that overcomes the shortcomings of previously used approaches. Future work can explore additional security features and extend the proposed approach to encrypt other forms of multimedia data.

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Text-to-Speech and Speech-to-Text Converter—Voice Assistant

Janokar,

Ratnaparkhi,

Rathi

et al. 2023

Lecture Notes in Networks and Systems

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Smart and Multi-Way Attendance Tracking System Using an Image-Processing Technique

Cited by 12 publications

References 8 publications

Vehicle detection in varied weather conditions using enhanced deep YOLO with complex wavelet

Vehicle detection in varied weather conditions using enhanced deep YOLO with complex wavelet

A Novel Dual Confusion and Diffusion Approach for Grey Image Encryption using Multiple Chaotic Maps

Text-to-Speech and Speech-to-Text Converter—Voice Assistant

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