Person: Khare, Manish
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Manish Khare
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Publication Metadata only Rule-based multi-view human activity recognition system in real time using skeleton data from RGB-D sensor(Springer, 01-03-2021) Varshney, Neeraj; Bakariya, Brijesh; Kushwaha, Alok Kumar Singh; Khare, Manish; DA-IICT, GandhinagarIdentification 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%.Publication Metadata only Editorial Note: Visual and Sensory Data Processing for Real Time Intelligent Surveillance System(Springer, 01-12-2022) Kushwaha, Alok Kumar Singh; Prakash, Om; Khare, Manish; Gwak, Jeonghwan; Nguyen, Thanh Binh; Khare, Ashish; DA-IICT, GandhinagarMultimedia Tools and Applications�gratefully acknowledges the editorial work of the scholars listed below on the special issue entitled �Visual and Sensory Data Processing for Real Time Intelligent Surveillance System� (SI 1220).Publication Metadata only Human detection in complex real scenes based on combination of biorthogonal wavelet transform and Zernike moments(Elsevier, 01-05-2018) Prakash, Om; Gwak, Jeonghwan; Khare, Manish; Khare, Ashish; Jeon, Moongu; DA-IICT, GandhinagarHuman detection in real scenes with high complexity is a crucial problem in�computer vision�research. For tackling the human detection task, several existing methods adopt one feature or combination of features to detect human objects. In this work, we propose a new combination of features based algorithm for human detection, which identifies the presence of a human, in complex real scenes. In the combination, the two features (i) biorthogonal�wavelet transform�(BWT) and (ii) Zernike moments (ZM) have been used. The approximate shift-invariance and�symmetry properties�of BWT facilitate the human detection in the�wavelet domain. Specifically, the shift-invariance property of BWT is effective for translated object representation whereas the symmetry property yields perfect reconstruction for retaining object boundaries (i.e., edges). Moreover, translation and rotation-invariance properties of ZM are especially beneficial for the representation of varying pose and orientation of the human objects. For these reasons, the composite of the two features brings about significant synthesized benefits over each�single feature�and the other widely used features. In the experiments for human detection, we used two classifiers,�AdaBoost�and�support vector machines, respectively, for the comparative study purpose, and the standard INRIA dataset and DaimlerChrysler dataset were used for the evaluations. Experimental results demonstrated the significant outperformance of the proposed method through�quantitative evaluations�and also suggest that the proposed hybridization of features is preferable for the classification problem.Publication Metadata only Human Activity Recognition Based On Video Summarization And Deep Convolutional Neural Network(Oxford University Press, 23-03-2024) Kushwaha, Arati; Khare, Manish; Bommisetty, Reddy Mounika; Khare, Ashish; DA-IICT, GandhinagarIn this technological era, human activity recognition (HAR) plays a significant role in several applications like surveillance, health services, Internet of Things, etc. Recent advancements in deep learning and video summarization have motivated us to integrate these techniques for HAR. This paper introduces a computationally efficient HAR technique based on a deep learning framework, which works well in realistic and multi-view environments. Deep convolutional neural networks (DCNNs) normally suffer from different constraints, including data size dependencies, computational complexity, overfitting, training challenges and vanishing gradients. Additionally, with the use of advanced mobile vision devices, the demand for computationally efficient HAR algorithms with the requirement of limited computational resources is high. To address these issues, we used integration of DCNN with video summarization using keyframes. The proposed technique offers a solution that enhances performance with efficient resource utilization. For this, first, we designed a lightweight and computationally efficient deep learning architecture based on the concept of identity skip connections (features reusability), which preserves the gradient loss attenuation and can handle the enormous complexity of activity classes. Subsequently, we employed an efficient keyframe extraction technique to minimize redundancy and succinctly encapsulate the entire video content in a lesser number of frames. To evaluate the efficacy of the proposed method, we performed the experimentation on several publicly available datasets. The performance of the proposed method is measured in terms of evaluation parameters Precision, Recall, F-Measure and Classification Accuracy. The experimental results demonstrated the superiority of the presented algorithm over other existing state-of-the-art methods.Publication Metadata only A comprehensive survey on person re-identification approaches: various aspects(Springer, 01-05-2022) Singh, Nikhil Kumar; Khare, Manish; Jethva, Harikrishna B; DA-IICT, GandhinagarPerson re-identification (Re-ID) is an application of video surveillance and has become popular among Computer Vision and Image processing research communities since last decade due to having its strong safety and security potential. It is the process of identifying a person of interest in distributed non-overlapping camera views. Person re-identification has broad application in maintaining the security by re-identifying the malicious persons in networking cameras. Now a days terrorist and criminal activities are increasing day by day and it is utmost important to re-identify a person of interest at public places like � shopping malls, railway stations, airports, huge public events etc. A lot of challenges are involved in the re-identification process like variation in lighting condition, different poses and viewpoints, blurring effect, image resolution, background changes etc. Basically 2 types of datasets (image based, video based) are designed for re-identification purpose based on application and approaches. This paper includes the study of many popular datasets like ViPER, iLIDS, Market1501, DukeMTMC4ReID, CUHK01, CHUK02, CHUK03, PRID2011 etc. including the various parameters (no of persons, no of images, no of cameras, size of frames etc.) and challenges involved in that. In this paper various aspects of person re-identification approaches are discussed including temporal, spatial, feature, distance metric, machine learning, automation etc. to get the comprehensive and exhaustive idea of person re-identification methods.Publication Metadata only Tracking of multiple human objects using a Combination of Daubechies complex wavelet transform and Zernike moment(ICTACT, 01-08-2020) Khare, Manish; Khare, Ashish; DA-IICT, GandhinagarThe goal of multi object tracking is to find location of the target objects in number of consecutive frames of a video. Tracking of multiple human objects in a scene is one of the challenging problems in computer vision applications due to illumination variation, object occlusion, abrupt motion etc. This paper introduces a new method for multiple human object tracking by exploiting the properties of Daubechies complex wavelet transform and Zernike moment. The proposed method uses combination of Daubechies complex wavelet transform and Zernike moment as a feature of objects. The motivation behind using combination of these two as a feature of object, because shift invariance and better edge representation properties make Daubechies complex wavelet transform suitable for locating object in consecutive frames whereas translation invariant property of Zernike moment is also helpful for correct object identification in consecutive frames. The proposed method is capable to handle full occlusion, partial occlusion, split and object re-enter problems. The experimental results validate the effectiveness and robustness of the proposed method.Publication Metadata only EEG/MEG source imaging in the absence of subject's brain MRI scan: Perspective on co-registration and MRI selection approach(Wiley, 01-01-2023) Gohel, Bakul; Khare, Manish; DA-IICT, GandhinagarEEG/MEG source localization requires a subject's brain MRI to compute the sourcemodel and headmodel.�As part of this computation, co-registration of the digitized head information and brain MRI scan is the essential step. However, in the absence of a brain MRI scan, an approximated sourcemodel and headmodel can be computed from the subject's digitized head information and brain MRI scans from other subjects. In the present work, we compared the fiducial (FID)- and iterative closet point (ICP)-based co-registration approaches for computing an approximated sourcemodel using single and multiple available brain MRI scans. We also evaluated the two different template MRI selection strategies: one is based on objective registration error, and another on sourcemodel approximation error. The outcome suggests that averaged approximated solutions using multiple template brain MRI scans showed better performance than single-template MRI-based solutions. The FID-based approach performed better than the ICP-based approach for co-registration of the digitized head surface and brain MRI scan. While selecting template MRIs, the selection approach based on objective registration error showed better performance than a sourcemodel approximation error-based criterion. Cross-dataset performance analysis showed a higher model approximation error than within-dataset analysis. In conclusion, the FID-based co-registration approach and objective registration error-based MRI selection criteria provide a simple, fast and more accurate solution to compute averaged approximated models compared with the ICP-based approach. The demography of brain MRI scans should be similar to that of the query subject whose brain MRI scan was unavailable.Publication Metadata only A Novel Algorithm for Efficient Utilization of GemStone using Genetic Algorithm(Springer, 14-01-2021) Sadani, Hiten M; Singh, Nikhil Kumar; Khare, Manish; DA-IICT, GandhinagarIn this paper, a novel method is used for fitting a polished and faceted object which is also called as a gem or diamond in a given rough gemstone using genetic algorithm. The goal of proposed Genetic Algorithm based Multiple Object Fitting algorithm is to maximize the utilization of the volume of rough gemstone by fitting the largest number of polished gemstones inside rough gemstone. One of the most difficult tasks is to fit the number of polished gemstones and positioning of each and every polished gemstone within the rough gemstone in order to minimize the waste. This is an optimization problem that is used to find the position, orientation, and scaling parameters of all the polished gemstones within a given rough gemstone such that the sum of volumes of all polished gemstones is maximized. The major novelty of proposed work is to fit more than one object in a given rough stone. The simulation results demonstrate the efficiency of our proposed algorithm.Publication Metadata only Combining Zernike moment and complex wavelet transform for human object classification(IJCVR, 05-01-2018) Khare, Manish; Prakash, Om; Srivastava, Rajneesh Kumar; DA-IICT, GandhinagarHuman object classification is an important problem for smart video surveillance, where we classify human object in real scenes. In this paper, we have proposed a method for human object classification, which classify the object present in a scene into one of the two classes: human and non-human. The proposed method uses combination of Daubechies complex wavelet transform and Zernike moment as a feature of object. The motivation behind using combination of these two as a features of object, because shift-invariance and better edge representation property makes Daubechies complex wavelet transform suitable for locating object, whereas rotation invariance property of Zernike moment is also helpful for correct object identification. We have used Adaboost as a classifier for classification of the objects. The proposed method has been tested on different standard dataset. Quantitative experimental evaluation result shows that the proposed method gives better performance than other state-of-the-art methods for human object classification.Publication Metadata only Integration of complex wavelet transform and Zernike moment for multi-class classification(Springer, 10-02-2021) Khare, Manish; Khare, Ashish; DA-IICT, GandhinagarMulticlass object classification is a crucial problem in computer vision research and have different emerging applications such as video surveillance. The task of multiclass object classification has more challenges because of highly variable nature and real time processing requirement of data. For tackling the multiclass object classification task, several existing methods adopt one feature or combination of features to classify objects. In this work, we propose a new combination of features-based algorithm for object classification. In the combination, the two features: (1) Daubechies complex wavelet transform (DCxWT) and (2) Zernike moments (ZM) have been used. The shift-invariance and symmetry properties of DCxWT facilitate the object classification in the wavelet domain. Specifically, the shift-invariance property of DCxWT is effective for translated object representation whereas the symmetry property yields perfect reconstruction for retaining object boundaries (i.e., edges). Moreover, translation and rotation-invariance properties of ZM are especially beneficial for the representation of varying pose and orientation of the objects. For these reasons, the composite of the two features brings about significant synthesized benefits over each single feature and the other widely used features. The multi-class support vector machine classifier is used for classifying different objects. The proposed method has been tested on standard datasets as well as our own dataset prepared by authors of this paper. Experimental results demonstrated the significant outperformance of the proposed method through quantitative evaluations and also suggest that the proposed hybridization of features is preferable for the classification problem.