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A Deep Learning Based Framework for Distracted Driver Detection 7th

A Deep Learning Based Framework for Distracted Driver Detect...

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7th National conference on computer vision, Pattern Recognition, image processing, and graphics, NCVPRIPG 2019

作者： Maurya, Swadesh Kumar Choudhary, Ayesha School of Computer and Systems Sciences Jawaharlal Nehru University New Delhi India

ISBN: (纸本)9789811586965

In this paper, we propose a novel, real-time, deep learning-based framework for distracted driver detection for driver Advanced Driver Assistance Systems (ADAS). We assume that the camera is assumed to be mounted inside the vehicle such that the side view of the driver is in view. Distracted driving is a serious problem leading to a large number of serious and even fatal road accidents worldwide every year. We propose a deep learning architecture that takes as input the captured images of the driver and classifies and recognizes the various distracted driving behaviors. It also recognizes if the driver is not distracted and is alert. the experiments are performed on the publicly available State Farm Distracted Driver Detection (SFDDD) dataset [1] which has 9 classes of distracted driver behavior and one class of alert driving. the training time for the proposed framework is minimal and approach works in real-time. Our experimental results show that our proposed framework is robust and performs better than the state-of-the-art approaches on this dataset. © 2020, Springer Nature Singapore Pte Ltd.

关键词： Advanced driver assistance systems

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Short local trajectory based moving anomaly detection 14

Short local trajectory based moving anomaly detection

引用

9th indian conference on computer vision, graphics and image processing, ICVGIP 2014

作者： Biswas, Sovan Venkatesh Babu, R. Indian Institute of Science Bangalore India

ISBN: (纸本)1595930361

A high level abstraction of the behavior a moving object can be obtained by analyzing its trajectory. However, traditional trajectories or tracklets are bound by the limitations of the underlying tracking algorithm used. In this paper, we propose a novel idea of detecting anomalous objects amid other moving objects in a video based on its short history. this history is defined as short local trajectory (SLT). the unique approach of generating SLTs from super-pixels belonging to a foreground object that incorporates both spatial and temporal information is the key in detection of anomaly. Additionally, the proposed trajectory extraction is robust across videos having different crowd density, occlusions, etc. Generally the trajectories of persons/objects moving at a particular region under usual conditions has certain fixed characteristics, thus we use Hidden Markov Model (HMM) for capturing the usual trajectory patterns during training. Whereas during detection, the proposed algorithm takes SLTs as observations for each super-pixel and measures its likelihood of being anomaly using the learned HMMs. Furthermore, we compute the spatial consistency measure for each SLT based on the neighboring trajectories. thus, anomaly detected by the proposed approach is highly localized as demonstrated from the experiments conducted on two widely used anomaly datasets, namely UCSD Ped1 and UCSD Ped2. Copyright 2014 ACM.

关键词： Trajectories

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Accelerating Native Inference Model Performance in Edge Devices using TensorRT 6

Accelerating Native Inference Model Performance in Edge Devi...

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6th IEEE International conference on Recent Advances in Intelligent Computational Systems, RAICS 2024

作者： Gopiktrishna, P.B. Marion Lincy G, Rubell Rishekeeshan, Abhishek Deekshitha Indian Institute of Information Technology Computer Science and Engineering Kottayam India AiRender Technology Pvt Ltd Bangalore India

ISBN: (纸本)9798350381689

In this era, deep learning is becoming increasingly popular for solving real-world problems. Due to the extremely high processing power required to execute the most complex deep learning models, graphics processing Units(GPUs) and Neural processing Units(NPUs) are essential for optimal execution. However, it is challenging to incorporate these high-performance processors in every edge device like Internet of things gadgets, embedded systems, and cell phones to execute complex inference models. Hence, deep learning models must be optimized to perform well on edge devices with limited memory, power, and computing capabilities. Optimizing the inference models to accelerate performance on edge devices is one of the significant issues facing, especially in complex computation domains like computer vision. TensorRT, introduced by NVIDIA, is one of the best approaches used to optimize inference models, by accelerating their performance on edge devices without compromising the model accuracy. It presents a variety of optimization approaches, including layer fusion, quantization, dynamic tensor memory management, kernel auto tuning, and precision calibration. Every optimization technique is contingent upon the problem that have selected and the use of all optimization approaches has the potential to significantly speed up the performance of an inference model. this paper explores how TensorRT accelerates the performance of inference models like ResNet-50, VGG16, and Inception V3 in an image classification problem, and the results are analyzed with its native inference models. the utilization of TensorRT in edge devices is expanding significantly, reflecting a growing scope for enhanced performance and efficiency in processing complex neural networks at the edge. © 2024 IEEE.

关键词： graphics processing unit

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Enhanced random forest for mitosis detection 14

Enhanced random forest for mitosis detection

引用

9th indian conference on computer vision, graphics and image processing, ICVGIP 2014

作者： Paul, Angshuman Mukherjee, Dipti Prasad Indian Statistical Institute 203 B. T. Road Kolkata700108 India

ISBN: (纸本)1595930361

Histopathological grading of cancer is a measure of the cell appearance in malignant neoplasms. Grading offers an in-sight to the growth of the cancer and helps in developing individual treatment plans. the Nottingham grading system [12], well known method for invasive breast cancer grading, primarily relies on the mitosis count in histopathological slides. Pathologists manually identify mitotic figures from a few thousand slide images for each patient to determine the grade of the cancer. Mitotic figures are hard to identify as the appearance of the mitotic cells change at different phases of mitosis. So, the manual cancer grading is not only a tedious job but also prone to observer variability. We propose a fast and accurate approach for automatic mitosis detection from histopathological images using an enhanced random forest classifier with weighted random trees. the random trees are assigned a tree penalty and a forest penalty depending on their classification performance at the training phase. the weight of a tree is calculated based on these penalties. the forest is trained through regeneration of population from weighted trees. the input data is classified based on weighted voting from the random trees after several populations. Experiments show at least 11 percent improvement in F1 score on more than 450 histopathological images at ×40 magnification. Copyright 2014 ACM.

关键词： Grading

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Fusion of image quality assessment and transfer learning for COVID19 detection using CT scan image 21

Fusion of image quality assessment and transfer learning for...

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12th indian conference on computer vision, graphics and image processing, ICVGIP 2021

作者： Kiruthika, S. Masilamani, V. Joshi, Pratik Indian Institute of Information Technology Design and Manufacturing Kancheepuram Tamilnadu Chennai India

ISBN: (纸本)9781450391276

One of the main challenges in controlling the spread of COVID19 pandemic is to diagnose infection early. the most reliable method RT - PCR takes several hours to give results. Although the Anti-Body (Serological) test gives the results in a few hours, it is not accurate, and hence it is not reliable. Moreover, they are invasive. Another issue with these methods is that the number of labs performing these tests are very limited. It will be beneficial if the already existing clinical infrastructure is used for diagnosing COVID19 accurately in real time. Recently chest CT images are used by researchers to diagnose the COVID19 with impressive accuracy. the state of the art method for detecting COVID19 using CT chest images involves Deep Learning. Deep Learning is expected to provide accurate and reliable results only when the model is trained on a large data set. Due to non-availability of a large data set the existing models have been trained on a smaller size data set. therefore it would be better to design a model to give good accuracy with reliability. To achieve accuracy along with reliability we proposed a COVID19 detection model with the combination of deep learning model and the traditional machine learning model. the novelty of the proposed model is the fusion of image quality and deep learning. the proposed method outperformed the state of the art method in terms of accuracy, recall and F1 score (more than 99 % in almost all the metrics) on a benchmark data set. the efficacy of the selected features and also explainability of the method are demonstrated through various tests. © 2021 ACM.

关键词： Diagnosis

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Objective evaluation of noisy multimodal medical image fusion using Daubechies complex wavelet transform 12

Objective evaluation of noisy multimodal medical image fusio...

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8th indian conference on computer vision, graphics and image processing, ICVGIP 2012

作者： Singh, Rajiv Khare, Ashish Department of Electronics and Communication University of Allahabad Allahabad India

ISBN: (纸本)9781450316606

Medical image fusion needs proper attention as images obtained from medical instruments are of poor contrast and corrupted by blur and noise due to imperfection of image capturing devices. thus, objective evaluation of medical image fusion techniques has become an important task in noisy domain. therefore, in the present work, we have proposed maximum selection and energy based fusion rules for the evaluation of noisy multimodal medical image fusion using Daubechies complex wavelet transform (DCxWT). Unlike, traditional real valued wavelet transforms, which suffered from shift sensitivity and did not provide any phase information, DCxWT is shift invariant and provides phase information through its imaginary coefficients. Shift invariance and availability of phase information properties of DCxWT have been found useful for fusion of multimodal medical images. the experiments have been performed over several set of noisy medical images at multiple levels of noise for the proposed fusion scheme. Further, the proposed fusion scheme has been tested up to the maximum level of Gaussian, salt & pepper and speckle noise. Objective evaluation of the proposed fusion scheme is performed with fusion factor, fusion symmetry, entropy, standard deviation and edge information metrics. Results have been shown for two sets of multimodal medical images for the proposed method with maximum and energy based fusion rules, and comparison has been done with Lifting wavelet transform (LWT) and Stationary wavelet transform (SWT) based fusion methods. Comparative analysis of the proposed method with LWT and SWT based fusion methods at different noise levels shows the superiority of the proposed scheme. Moreover, the plots of different fusion metrics against the maximum level of Gaussian, salt & pepper and speckle noise show the robustness of the proposed fusion method against noise. © 2012 ACM.

关键词： image fusion

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Multi-criteria energy minimization with boundedness, edge-density and rarity, for object saliency in natural images 14

Multi-criteria energy minimization with boundedness, edge-de...

引用

9th indian conference on computer vision, graphics and image processing, ICVGIP 2014

作者： Roy, Sudeshna Das, Sukhendu Indian Institute Technology Madras India

ISBN: (纸本)1595930361

Recent methods of bottom-up salient object detection have attempted to either: (i) obtain a probability map with a 'contrast rarity' based functional, formed using low level cues;or (ii) Minimize an objective function, to detect the object. Most of these methods fail for complex, natural scenes, such as the PASCAL-VOC challenge dataset which contains images with diverse appearances, illumination conditions, multiple distracting objects and varying scene environments. We thus formulate a novel multi-criteria objective function which captures many dependencies and the scene structure for correct spatial propagation of low-level priors to perform salient object segmentation, in such cases. Our proposed formulation is based on CRF modeling where the minimization is performed using graph cut and the optimal parameters of the objective function are learned using a max-margin framework from the training set, without the use of class labels. Hence the method proposed is unsu-pervised, and works efficiently when compared to the very recent state-of-the art methods of saliency map detection and object proposals. Results, compared using F-measure and intersection-over-union scores, show that the proposed method exhibits superior performance in case of the complex PASCAL-VOC 2012 object segmentation dataset as well as the traditional MSRA-B saliency dataset. Copyright 2014 ACM.

关键词： Object detection

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Tracking of retinal microsurgery tools using late fusion of responses from convolutional neural network over pyramidally decomposed frames 10

Tracking of retinal microsurgery tools using late fusion of ...

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10th indian conference on computer vision, graphics and image processing, ICVGIP 2016

作者： Mishra, Kaustuv Sathish, Rachana Sheet, Debdoot Indian Institute of Technology Kharagpur Kharagpur India

computer vision and robotic assistance are increasingly being used to improve the quality of surgical interventions. Tool tracking becomes critical in interventions viz. endoscopy, laparoscopy and retinal microsurgery (RM) where unlike open surgery the surgeons do not have direct visual and physical access to the surgical site. RM is performed using miniaturized tools and requires careful observation through a surgical microscope by the surgeon. Tracking of surgical tools primarily provides robotic assistance during surgery and also serves as a means to assess the quality of surgery, which is extremely useful during surgical training. In this paper we propose a deep learning based visual tracking of surgical tool using late fusion of responses from convolutional neural network (CNN) which comprises of 3 steps: (i) training of CNN for localizing the tool tip on a frame (ii) coarsely estimating the tool tip region using the trained CNN and (iii) a finer search around the estimated region to accurately localize the tool tip. Scale invariant tracking of tool is ensured by incorporating multi-scale late fusion where the CNN responses are obtained at each level of the Gaussian scale decomposition pyramid. Performance of the proposed method is experimentally validated on the publicly available retinal microscopy instrument tracking (RMIT) dataset (https://***. com/site/sznitr/code-and-datasets). Our method tracks tools with a maximum accuracy of 99.13% which substantiates the efficacy of the proposed method in comparison to existing approaches. © Springer International Publishing AG 2017.

关键词： Convolution

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Automatic text area segmentation in natural images

Automatic text area segmentation in natural images

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IEEE International conference on image processing

作者： Syed Ali Raza Jafri Mireille Boutin Edward J. Delp School of Electrical and Computer Engineering Purdue University West Lafayette IN USA

We present a hierarchical method for segmenting text areas in natural images. the method assumes that the text is written with a contrasting color on a more or less uniform background. No assumption is made regarding the language or character set used to write the text. In particular, the text can contain simple graphics or symbols. the key feature of our approach is that we first concentrate on finding the background of the text, before testing whether there is actually text on the background. Since uniform areas are easy to find in natural images, and since text backgrounds define areas which contain "holes" (where the text is written) we thus look for uniform areas containing "holes" and label them as text backgrounds candidates. Each candidate area is then further tested for the presence of text within its convex hull. We tested our method on a database of 65 images including English and Urdu text. the method correctly segmented all the text areas in 63 of these images, and in only 4 of these were areas that do not contain text also segmented.

关键词： image segmentation Testing graphics Shape image databases Wire Robustness computer vision Design methodology Licenses

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An Algorithm to Calculate Retinal Vessel Diameter in Fundus images 14

An Algorithm to Calculate Retinal Vessel Diameter in Fundus ...

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14th indian conference on computer vision, graphics and image processing, ICVGIP 2023

作者： Yoga Sri Varshan, V. Sahayam, Subin Jayaraman, Umarani Indian Institute of Information Technology Design and Manufacturing Kancheepuram Tamil Nadu Chennai India

ISBN: (纸本)9798400716256

Retinal blood vessels are the arteries and veins that supply blood to the human eye. Fundus images obtained through a fundus camera capture retinal information like, the macula, optic disc, cup, fovea, retinal blood vessels, and abnormalities. the retinal blood vessels are miniature and are usually measured in micrometers. It is difficult and time-consuming to study retinal vessels from a fundus image. Generally, a detailed vessel study requires costly equipment like Ocular Coherence Tomography (OCT) and is comparatively less available at hospitals than a fundus camera. Blood vessels are connected throughout the body. Studying retinal blood vessel health can help ophthalmologists and doctors understand the overall health of blood vessels across the body non-invasively. Narrowing of blood vessels can make a person prone to many diseases, like hypertension, cardiovascular disease, and stroke. A few methods aim to automate the task using computer Aided Diagnosis (CAD). the work proposes a novel and relatively less complex algorithm for calculating the diameter of a given retinal blood vessel at a given distance from the optic disc center in a fundus image. the approach aims to compute the artery-to-vein diameter ratio of retinal blood vessels at a given distance from the center of the optic disc. the proposed method has been tested on the LES-AV 2020 dataset and it achieved an approximate artery-to-vein diameter ratio (AVR) of 2:3, which is in line with the average healthy AVR in the literature. therefore, the proposed method is accurate and can be extended to find AVR in various clinical scenarios to detect and study any abnormalities. © 2023 Copyright held by the owner/author(s). Publication rights licensed to ACM.

关键词： Blood vessels

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