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检索条件"任意字段=Proceedings of the Thirteenth Indian Conference on Computer Vision, Graphics and Image Processing"

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Hierarchical Traffic Sign Recognition for Autonomous Driving 9th

Hierarchical Traffic Sign Recognition for Autonomous Driving

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9th International conference on Pattern Recognition Applications and Methods (ICPRAM)

作者： Sengar, Vartika Rameshan, Renu M. Ponkumar, Senthil Indian Inst Technol Sch Comp & Engn Mandi Himachal Prades India Continental Automot Components Pvt Ltd Bengaluru Karnataka India

ISBN: (纸本)9789897583971

Traffic Sign Recognition is very crucial for self-driving cars and Advanced Driver Assistance Systems. As the vehicle moves within a region or across regions, it encounters a variety of signs which needs to be recognized with very high accuracy. It is generally observed that traffic signs have large intra-class variability and small inter-class variability. This makes visual distinguishability between distinct classes extremely irregular. In this paper we propose a hierarchical classifier in which the number of coarse classes is automatically determined. This gives the advantage of dedicated classifiers trained for classes which are more difficult to distinguish. This is an application oriented work which involves systematic and intelligent combination of machine learning and computer vision based algorithms with required modifications for designing fully automated hierarchical classification framework for traffic sign recognition. The proposed solution is a real-time scalable machine learning based approach which can efficiently take care of wide intra-class variations without extracting desired handcrafted features beforehand. It eliminates the need for manually observing and grouping relevant features, thereby reducing human time and efforts. The classifier performance accuracy is surpassing the accuracy achieved by humans on publicly available GTSRB traffic sign dataset with lesser parameters than the existing solutions.

关键词： Hierarchical Classification Spectral Clustering Convolutional Neural Networks Machine Learning Data processing image processing Pattern Recognition

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Compressive Sensing Based Privacy for Fall Detection 7th

Compressive Sensing Based Privacy for Fall Detection

引用

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

作者： Gupta, Ronak Anand, Prashant Chaudhury, Santanu Lall, Brejesh Singh, Sanjay Department of Electrical Engineering Indian Institute of Technology Delhi New Delhi India Indian Institute of Technology Jodhpur Jheepasani India Cognitive Computing Group CSIR-CEERI Pilani India

ISBN: (纸本)9789811586965

Fall detection holds immense importance in the field of healthcare, where timely detection allows for instant medical assistance. In this context, we propose a 3D ConvNet architecture which consists of 3D Inception modules for fall detection. The proposed architecture is a custom version of Inflated 3D (I3D) architecture, that takes compressed measurements of video sequence as spatio-temporal input, obtained from compressive sensing framework, rather than video sequence as input, as in the case of I3D convolutional neural network. This is adopted since privacy raises a huge concern for patients being monitored through these RGB cameras. The proposed framework for fall detection is flexible enough with respect to a wide variety of measurement matrices. Ten action classes randomly selected from Kinetics-400 with no fall examples, are employed to train our 3D ConvNet post compressive sensing with different types of sensing matrices on the original video clips. Our results show that 3D ConvNet performance remains unchanged with different sensing matrices. Also, the performance obtained with Kinetics pre-trained 3D ConvNet on compressively sensed fall videos from benchmark datasets is better than the state-of-the-art techniques. © 2020, Springer Nature Singapore Pte Ltd.

关键词： Compressed sensing

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A Chaos Based Robust and Secure image Hashing Framework 18

A Chaos Based Robust and Secure Image Hashing Framework

引用

proceedings of the 11th indian conference on computer vision, graphics and image processing

作者： Satendra Pal Singh Gaurav Bhatnagar Indian Institute of Technology Jodhpur India

ISBN: (纸本)9781450366151

In this paper, a robust image hashing framework is presented using discrete cosine transformation and singular value decomposition. Firstly, the input image is normalized using geometric moment and normalized coefficients are divided into non-overlapping blocks. The selected blocks based on a peace-wise non-linear chaotic map are transformed using discrete cosine transom followed by singular value decomposition. Then a feature matrix is constructed in reliance on Hessian matrix and the final hash values are obtained. The proposed hashing system is resilient to different content-preserving image distortions such as geometric and filtering operations. The simulated results demonstrate the efficiency proposed framework in terms of security and robustness.

关键词： Discrete cosine transformation

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Exploring Temporal Differences in 3D Convolutional Neural Networks 7th

Exploring Temporal Differences in 3D Convolutional Neural Ne...

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

作者： Kanojia, Gagan Kumawat, Sudhakar Raman, Shanmuganathan Indian Institute of Technology Gandhinagar Gandhinagar India

ISBN: (纸本)9789811586965

Traditional 3D convolutions are computationally expensive, memory intensive, and due to large number of parameters, they often tend to overfit. On the other hand, 2D CNNs are less computationally expensive and less memory intensive than 3D CNNs and have shown remarkable results in applications like image classification and object recognition. However, in previous works, it has been observed that they are inferior to 3D CNNs when applied on a spatio-temporal input. In this work, we propose a convolutional block which extracts the spatial information by performing a 2D convolution and extracts the temporal information by exploiting temporal differences, i.e., the change in the spatial information at different time instances, using simple operations of shift, subtract and add without utilizing any trainable parameters. The proposed convolutional block has same number of parameters as of a 2D convolution kernel of size, and has n times lesser parameters than an 3D convolution kernel. We show that the 3D CNNs perform better when the 3D convolution kernels are replaced by the proposed convolutional blocks. We evaluate the proposed convolutional block on UCF101 and ModelNet datasets. All the codes and pretrained models are publicly available at https://***/GaganKanojia/SSA-ResNet. © 2020, Springer Nature Singapore Pte Ltd.

关键词： Object recognition

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DeepPFCN: Deep Parallel Feature Consensus Network for Person Re-identification 7th

DeepPFCN: Deep Parallel Feature Consensus Network for Person...

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

作者： Singh, Shubham Kumar Miyapuram, Krishna P. Raman, Shanmuganathan Indian Institute of Technology Gandhinagar PalajGujarat382355 India

ISBN: (纸本)9789811586965

Person re-identification aims to associate images of the same person over multiple non-overlapping camera views at different times. Depending on the human operator, manual re-identification in large camera networks is highly time consuming and erroneous. Automated person re-identification is required due to the extensive quantity of visual data produced by rapid inflation of large scale distributed multi-camera systems. The state-of-the-art works focus on learning and factorize person appearance features into latent discriminative factors at multiple semantic levels. We propose Deep Parallel Feature Consensus Network (DeepPFCN), a novel network architecture that learns multi-scale person appearance features using convolutional neural networks. This model factorizes the visual appearance of a person into latent discriminative factors at multiple semantic levels. Finally consensus is built. The feature representations learned by DeepPFCN are more robust for the person re-identification task, as we learn discriminative scale-specific features and maximize multi-scale feature fusion selections in multi-scale image inputs. We further exploit average and max pooling in separate scale for person-specific task to discriminate features globally and locally. We demonstrate the re-identification advantages of the proposed DeepPFCN model over the state-of-the-art re-identification methods on three benchmark datasets - Market1501, DukeMTMCreID, and CUHK03. We have achieved mAP results of 75.8%, 64.3%, and 52.6% respectively on these benchmark datasets. © 2020, Springer Nature Singapore Pte Ltd.

关键词： Network architecture

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Scene Adaptive Cosmetic Makeup Transfer 18

Scene Adaptive Cosmetic Makeup Transfer

引用

proceedings of the 11th indian conference on computer vision, graphics and image processing

作者： Rishubh Parihar Aniket Dashpute Prem Kalra Indian Institute of Technology Delhi Hauz Khas New Delhi

ISBN: (纸本)9781450366151

Cosmetic makeup is an art in itself which people use to often enhance their beauty and express themselves. Putting on makeup is a tedious task, so who would not love to see themselves in makeup before physically applying it? In this work, we demonstrate the transfer of makeup from a reference makeup image to some subject image. Our technique involves generation and use of 3D face models of these images. Some nice features of our pipeline include: (1) process adapts to the lighting conditions of the subject image. (2) after makeup on subject image, it is possible to view it in different indoor and outdoor lighting conditions. (3) accessories can be added realistically with proper lighting so that they naturally fit in the subject's *** show that these also make our makeup look very realistic and improved compared to many existing *** main advantage of our novel pipeline is the requirement of only the subject and reference images as input. This is unlike other techniques using 3D models which require extensive data collection.

关键词： computer graphics

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Dynamic Gesture Recognition with Pose-based CNN Features derived from videos using LSTM 18

Dynamic Gesture Recognition with Pose-based CNN Features der...

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proceedings of the 11th indian conference on computer vision, graphics and image processing

作者： Kankana Roy Rajiv R. Sahay Computational Vision Laboratory Department of Computer Science and Engineering Indian Institute of Technology Kharagpur Computational Vision Laboratory Department of Electrical Engineering Indian Institute of Technology Kharagpur

ISBN: (纸本)9781450366151

In this work, we address the problem of dynamic gesture recognition using a pose based video descriptor. The proposed approach takes as input video frames and extracts pose-specific image regions which are further processed by a pre-trained Convolutional Neural Network (CNN) to derive a pose-based descriptor for each frame. A Long Short Term Memory (LSTM) network is trained from scratch for dynamic gesture classification by learning long-term spatiotemporal relations among features. We also demonstrate that only using video data (RGB frames and optical flow) one can design an effective model for recognizing dynamic gestures. We utilize ChaLearn multi-modal gesture challenge dataset [13] and Cambridge hand gesture dataset [18] for evaluation of the proposed algorithm achieving an accuracy of 91.27% and 96% respectively using only RGB data.

关键词： Long short term memory

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Multimodal Egocentric Activity Recognition Using Multi-stream CNN 2018

Multimodal Egocentric Activity Recognition Using Multi-strea...

引用

proceedings of the 11th indian conference on computer vision, graphics and image processing

作者： Javed Imran Balasubramanian Raman Department of Computer Science and Engineering Indian Institute of Technology Roorkee Roorkee India

ISBN: (纸本)9781450366151

Egocentric activity recognition (EAR) is an emerging area in the field of computer vision research. Motivated by the current success of Convolutional Neural Network (CNN), we propose a multi-stream CNN for multimodal egocentric activity recognition using visual (RGB videos) and sensor stream (accelerometer, gyroscope, etc.). In order to effectively capture the spatio-temporal information contained in RGB videos, two types of modalities are extracted from visual data: Approximate Dynamic image (ADI) and Stacked Difference image (SDI). These image-based representations are generated both at clip level as well as entire video level, and are then utilized to finetune a pretrained 2D-CNN called MobileNet, which is specifically designed for mobile vision applications. Similarly for sensor data, each training sample is divided into three segments, and a deep 1D-CNN network is trained (corresponding to each type of sensor stream) from scratch. During testing, the softmax scores of all the streams (visual + sensor) are combined by late fusion. The experiments performed on multimodal egocentric activity dataset demonstrates that our proposed approach can achieve state-of-the-art results, outperforming the current best handcrafted and deep learning based techniques.

关键词： Stacked Difference image Dynamic image Egocentric Activity Recognition Convolutional Neural Network Multimodal Fusion

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4th Workshop on Document Analysis and Recognition, DAR 2018, held in Conjunction with the 11th indian conference on vision, graphics, and image processing, ICVGIP 2018

4th Workshop on Document Analysis and Recognition, DAR 2018,...

引用

4th Workshop on Document Analysis and Recognition, DAR 2018, held in Conjunction with the 11th indian conference on vision, graphics, and image processing, ICVGIP 2018

ISBN: (纸本)9789811393600

The proceedings contain 14 papers. The special focus in this conference is on Document Analysis and Recognition. The topics include: Word-Wise Handwriting Based Gender Identification Using Multi-Gabor Response Fusion;a Secure and Light Weight User Authentication System Based on Online Signature Verification for Resource Constrained Mobile Networks;benchmark Datasets for Offline Handwritten Gurmukhi Script Recognition;benchmark Dataset: Offline Handwritten Gurmukhi City Names for Postal Automation;attributed Paths for Layout-Based Document Retrieval;textual Content Retrieval from Filled-in Form images;A Study on the Effect of CNN-Based Transfer Learning on Handwritten Indic and Mixed Numeral Recognition;symbol Spotting in Offline Handwritten Mathematical Expressions;online Handwritten Bangla Character Recognition Using Frechet Distance and Distance Based Features;an Efficient Multi Lingual Optical Character Recognition System for indian Languages Through Use of Bharati Script;telugu Word Segmentation Using Fringe Maps;an Efficient Character Segmentation Algorithm for Connected Handwritten Documents.

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Real-time Simulation of Human Musculature 2018

Real-time Simulation of Human Musculature

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proceedings of the 11th indian conference on computer vision, graphics and image processing

作者： Neeladrishekhar Kanjilal Parag Chaudhuri Dept. of Computer Science & Engg. IIT Bombay

ISBN: (纸本)9781450366151

We present a reduced model based on position based dynamics for real-time simulation of human musculature. We demonstrate our methods on the muscles of the human arm. Co-simulation of all the muscles of the human arm allow us to accurately track the development of stresses and strains in the muscles, when the arm is moved. We evaluate our method for accuracy by comparing it with gold standard simulation models based on finite volume methods, and demonstrate the stability of the method under flexion, extension and torsion.

关键词： Human muscle simulation position based dynamics shape matching

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