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98 results on '"Kushwaha, Alok Kumar Singh"'

5. Integrating Spatial and Temporal Contextual Information for Improved Video Visualization

Author

Singh, Pratibha, Kushwaha, Alok Kumar Singh, Kacprzyk, Janusz, Series Editor, Gomide, Fernando, Advisory Editor, Kaynak, Okyay, Advisory Editor, Liu, Derong, Advisory Editor, Pedrycz, Witold, Advisory Editor, Polycarpou, Marios M., Advisory Editor, Rudas, Imre J., Advisory Editor, Wang, Jun, Advisory Editor, Kumar, Sandeep, editor, Balachandran, K., editor, Kim, Joong Hoon, editor, and Bansal, Jagdish Chand, editor

Published
2024
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6. Leveraging Natural Language Queries for Effective Video Analysis

Author

Singh, Pratibha, Kushwaha, Alok Kumar Singh, Kacprzyk, Janusz, Series Editor, Gomide, Fernando, Advisory Editor, Kaynak, Okyay, Advisory Editor, Liu, Derong, Advisory Editor, Pedrycz, Witold, Advisory Editor, Polycarpou, Marios M., Advisory Editor, Rudas, Imre J., Advisory Editor, Wang, Jun, Advisory Editor, Sharma, Harish, editor, Chakravorty, Antorweep, editor, Hussain, Shahid, editor, and Kumari, Rajani, editor

Published
2024
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7. A Comprehensive Analysis on Inter-Frame and Intra-Frame Video Forgery Detection Techniques

Author

Vaishnav, Pritha, Kushwaha, Alok Kumar Singh, Pandey, Raksha, Angrisani, Leopoldo, Series Editor, Arteaga, Marco, Series Editor, Chakraborty, Samarjit, Series Editor, Chen, Jiming, Series Editor, Chen, Shanben, Series Editor, Chen, Tan Kay, Series Editor, Dillmann, Rüdiger, Series Editor, Duan, Haibin, Series Editor, Ferrari, Gianluigi, Series Editor, Ferre, Manuel, Series Editor, Jabbari, Faryar, Series Editor, Jia, Limin, Series Editor, Kacprzyk, Janusz, Series Editor, Khamis, Alaa, Series Editor, Kroeger, Torsten, Series Editor, Li, Yong, Series Editor, Liang, Qilian, Series Editor, Martín, Ferran, Series Editor, Ming, Tan Cher, Series Editor, Minker, Wolfgang, Series Editor, Misra, Pradeep, Series Editor, Mukhopadhyay, Subhas, Series Editor, Ning, Cun-Zheng, Series Editor, Nishida, Toyoaki, Series Editor, Oneto, Luca, Series Editor, Panigrahi, Bijaya Ketan, Series Editor, Pascucci, Federica, Series Editor, Qin, Yong, Series Editor, Seng, Gan Woon, Series Editor, Speidel, Joachim, Series Editor, Veiga, Germano, Series Editor, Wu, Haitao, Series Editor, Zamboni, Walter, Series Editor, Zhang, Junjie James, Series Editor, Tan, Kay Chen, Series Editor, Kumar, Amit, editor, and Mozar, Stefan, editor

Published
2024
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8. Binary Classification for Video Forgery Detection Using REWIND Data Set

Author

Pandey, Raksha, Kushwaha, Alok Kumar Singh, Keerthi, Lavanuru Lakshmi, Yogesh, Madiri, Naveen, Bathakala, Angrisani, Leopoldo, Series Editor, Arteaga, Marco, Series Editor, Chakraborty, Samarjit, Series Editor, Chen, Jiming, Series Editor, Chen, Shanben, Series Editor, Chen, Tan Kay, Series Editor, Dillmann, Rüdiger, Series Editor, Duan, Haibin, Series Editor, Ferrari, Gianluigi, Series Editor, Ferre, Manuel, Series Editor, Jabbari, Faryar, Series Editor, Jia, Limin, Series Editor, Kacprzyk, Janusz, Series Editor, Khamis, Alaa, Series Editor, Kroeger, Torsten, Series Editor, Li, Yong, Series Editor, Liang, Qilian, Series Editor, Martín, Ferran, Series Editor, Ming, Tan Cher, Series Editor, Minker, Wolfgang, Series Editor, Misra, Pradeep, Series Editor, Mukhopadhyay, Subhas, Series Editor, Ning, Cun-Zheng, Series Editor, Nishida, Toyoaki, Series Editor, Oneto, Luca, Series Editor, Panigrahi, Bijaya Ketan, Series Editor, Pascucci, Federica, Series Editor, Qin, Yong, Series Editor, Seng, Gan Woon, Series Editor, Speidel, Joachim, Series Editor, Veiga, Germano, Series Editor, Wu, Haitao, Series Editor, Zamboni, Walter, Series Editor, Zhang, Junjie James, Series Editor, Tan, Kay Chen, Series Editor, Kumar, Amit, editor, and Mozar, Stefan, editor

Published
2024
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12. Role of Deep Learning in Tumor Malignancy Identification and Classification

Author

Chandni, Sachdeva, Monika, Kushwaha, Alok Kumar Singh, Kacprzyk, Janusz, Series Editor, Gomide, Fernando, Advisory Editor, Kaynak, Okyay, Advisory Editor, Liu, Derong, Advisory Editor, Pedrycz, Witold, Advisory Editor, Polycarpou, Marios M., Advisory Editor, Rudas, Imre J., Advisory Editor, Wang, Jun, Advisory Editor, Tistarelli, Massimo, editor, Dubey, Shiv Ram, editor, Singh, Satish Kumar, editor, and Jiang, Xiaoyi, editor

Published
2023
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17. Proposed Framework for Hybrid Approach Using Convolution LSTM-RNN for Human Activity Recognition

Author

Jindal, Sonika, Singh, Jaswinder, Sachdeva, Monika, Kushwaha, Alok Kumar Singh, Kacprzyk, Janusz, Series Editor, Gomide, Fernando, Advisory Editor, Kaynak, Okyay, Advisory Editor, Liu, Derong, Advisory Editor, Pedrycz, Witold, Advisory Editor, Polycarpou, Marios M., Advisory Editor, Rudas, Imre J., Advisory Editor, Wang, Jun, Advisory Editor, Saini, H. S., editor, Sayal, Rishi, editor, Govardhan, A., editor, and Buyya, Rajkumar, editor

Published
2022
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19. A Systematic Analysis of the Human Activity Recognition Systems for Video Surveillance

Author

Jindal, Sonika, Sachdeva, Monika, Kushwaha, Alok Kumar Singh, Kacprzyk, Janusz, Series Editor, Gomide, Fernando, Advisory Editor, Kaynak, Okyay, Advisory Editor, Liu, Derong, Advisory Editor, Pedrycz, Witold, Advisory Editor, Polycarpou, Marios M., Advisory Editor, Rudas, Imre J., Advisory Editor, Wang, Jun, Advisory Editor, Nayak, Padmalaya, editor, Pal, Souvik, editor, and Peng, Sheng-Lung, editor

Published
2022
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25. Delving Deeper with Dual-Stream CNN for Activity Recognition

Author

Chandni, Khurana, Rajat, Kushwaha, Alok Kumar Singh, Angrisani, Leopoldo, Series Editor, Arteaga, Marco, Series Editor, Panigrahi, Bijaya Ketan, Series Editor, Chakraborty, Samarjit, Series Editor, Chen, Jiming, Series Editor, Chen, Shanben, Series Editor, Chen, Tan Kay, Series Editor, Dillmann, Ruediger, Series Editor, Duan, Haibin, Series Editor, Ferrari, Gianluigi, Series Editor, Ferre, Manuel, Series Editor, Hirche, Sandra, Series Editor, Jabbari, Faryar, Series Editor, Jia, Limin, Series Editor, Kacprzyk, Janusz, Series Editor, Khamis, Alaa, Series Editor, Kroeger, Torsten, Series Editor, Liang, Qilian, Series Editor, Ming, Tan Cher, Series Editor, Minker, Wolfgang, Series Editor, Misra, Pradeep, Series Editor, Möller, Sebastian, Series Editor, Mukhopadhyay, Subhas, Series Editor, Ning, Cun-Zheng, Series Editor, Nishida, Toyoaki, Series Editor, Pascucci, Federica, Series Editor, Qin, Yong, Series Editor, Seng, Gan Woon, Series Editor, Veiga, Germano, Series Editor, Wu, Haitao, Series Editor, Zhang, Junjie James, Series Editor, Martin, Ferran, Series Editor, Speidel, Joachim, Series Editor, Khare, Ashish, editor, Tiwary, Uma Shankar, editor, Sethi, Ishwar K., editor, and Singh, Nar, editor

Published
2019
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27. IMF-MF: Interactive moment localization with adaptive multimodal fusion and self-attention.

Author

Singh, Pratibha, Kushwaha, Alok Kumar Singh, and Varshney, Neeraj

Abstract

Precise video moment retrieval is crucial for enabling users to locate specific moments within a large video corpus. This paper presents Interactive Moment Localization with Multimodal Fusion (IMF-MF), a novel interactive moment localization with multimodal fusion model that leverages the power of self-attention to achieve state-of-the-art performance. IMF-MF effectively integrates query context and multimodal features, including visual and audio information, to accurately localize moments of interest. The model operates in two distinct phases: feature fusion and joint representation learning. The first phase dynamically calculates fusion weights for adapting the combination of multimodal video content, ensuring that the most relevant features are prioritized. The second phase employs bi-directional attention to tightly couple video and query features into a unified joint representation for moment localization. This joint representation captures long-range dependencies and complex patterns, enabling the model to effectively distinguish between relevant and irrelevant video segments. The effectiveness of IMF-MF is demonstrated through comprehensive evaluations on three benchmark datasets: TVR for closed-world TV episodes and Charades for open-world user-generated videos, DiDeMo dataset, Open-world, diverse video moment retrieval dataset. The empirical results indicate that the proposed approach surpasses existing state-of-the-art methods in terms of retrieval accuracy, as evaluated by metrics like Recall (R1, R5, R10, and R100) and Intersection-of-Union (IoU). The results consistently demonstrate IMF-MF’s superior performance compared to existing state-of-the-art methods, highlighting the benefits of its innovative interactive moment localization approach and the use of self-attention for feature representation and attention modeling. [ABSTRACT FROM AUTHOR]

Published
2024
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30. A Framework of Moving Object Segmentation in Maritime Surveillance Inside a Dynamic Background

Author

Kushwaha, Alok Kumar Singh, Srivastava, Rajeev, Gavrilova, Marina, Editor-in-chief, Tan, C.J. Kenneth, Editor-in-chief, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Weikum, Gerhard, Series editor, Gavrilova, Marina L., editor, Saeed, Khalid, editor, Chaki, Nabendu, editor, and Shaikh, Soharab Hossain, editor

Published
2015
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36. Rule-based multi-view human activity recognition system in real time using skeleton data from RGB-D sensor.

Author

Varshney, Neeraj, Bakariya, Brijesh, Kushwaha, Alok Kumar Singh, and Khare, Manish

Subjects
HUMAN activity recognition, SKELETON, COMPUTER vision, VISUAL fields
Abstract

Identification of human activity with decent precision is a challenging task in the field of computer vision, especially when applying for surveillance purpose. A rule-based classifier method is proposed in this paper, which is capable of recognizing a view-invariant multiple human activity recognition in real time. A single Kinect sensor is used for the input of RGB-D data in real time. Initially, a skeleton-tracking algorithm is applied. After tracking the skeletons, activities are recognized from each individually tracked skeleton independently. Different rules are defined to recognize discrete skeleton positions and classify a particular order of multiple postures into activities. During the experimentation, we examine about 14 activities and found that the proposed method is robust and efficient concerning multiple views, scaling and phase variation activities during different realistic acts. A self-generated dataset in the controlled environment is used for the experiment. About 2 min of data was collected. Data from two different males were collected for multiple human activities. Experimental results show that the proposed method is flexible and efficient for multiple view activities as well as scale and phase variation activities. It provides a detection accuracy of 98%. [ABSTRACT FROM AUTHOR]

Published
2023
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39. Effective Brain Tumor Image Classification using Deep Learning

Author

Chandni, Sachdeva, Monika, and Kushwaha, Alok Kumar Singh

Abstract

A brain tumor can be fatal if it is not detected early. Manual diagnosis necessitates the radiologist’s expertise and experience, and it is also an error-prone and time-consuming approach. To guarantee a precise diagnosis, an efficient solution is required. This paper proposes an effective automated tumor diagnosis approach to achieve that objective. Firstly, brains MRIs (magnetic resonance imaging) are pre-processed, and the dataset is augmented to create a robust model. Then, features are extracted and classified using proposed IRConvNet model to label the MR images as healthy or malignant (tumor). The IRConvNet is convolutional network model created by bootstrapping the classification network on top of InceptionResNet architecture. The model possesses the ability to learn visual features at different scales. Compared to existing binary classification approaches, the proposed approach achieved an accuracy of more than 99%.

Published
2023
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48. A Dual Stream Model for Activity Recognition: Exploiting Residual- CNN with Transfer Learning.

Author

Singh, Roshan, Khurana, Rajat, Kushwaha, Alok Kumar Singh, and Srivastava, Rajeev

Subjects
HUMAN activity recognition, COMPUTER vision, ARTIFICIAL neural networks, SPATIOTEMPORAL processes, IMAGE databases
Abstract

Visual content has a protagonist role in this age of data revolution. These days, computer vision research community is fascinated towards application of convolution neural networks and transfer learning for various image and video analysis tasks. Residual connection in CNN can facilitate the training process in the deep networks. This paper investigates and uses deep residual networks with fusion based dual stream pre-trained models for activity recognition from video streams. The architecture is further trained and evaluated using standard video actions benchmarks of UCF-101, HMDB-51 and NTU RGB. Performance of depth-based variants of residual networks is also analysed. The proposed approach not only provides competitive results but also better at exploiting pre-trained model and annotated image data. [ABSTRACT FROM AUTHOR]

Published
2021
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