Soharab Hossain Shaikh

Bio: Soharab Hossain Shaikh is an academic researcher from BML Munjal University. The author has contributed to research in topics: Bengali & Computer science. The author has an hindex of 10, co-authored 39 publications receiving 494 citations. Previous affiliations of Soharab Hossain Shaikh include University of Calcutta & NIIT.

A comprehensive survey of human action recognition with spatio-temporal interest point (STIP) detector

Abstract: Over the past two decades, human action recognition from video has been an important area of research in computer vision. Its applications include surveillance systems, human---computer interactions and various real-world applications where one of the actor is a human being. A number of review works have been done by several researchers in the context of human action recognition. However, it is found that there is a gap in literature when it comes to methodologies of STIP-based detector for human action recognition. This paper presents a comprehensive review on STIP-based methods for human action recognition. STIP-based detectors are robust in detecting interest points from video in spatio-temporal domain. This paper also summarizes related public datasets useful for comparing performances of various techniques.

Moving Object Detection Using Background Subtraction

Abstract: This Springer Brief presents a comprehensive survey of the existing methodologies of background subtraction methods. It presents a framework for quantitative performance evaluation of different approaches and summarizes the public databases available for research purposes. This well-known methodology has applications in moving object detection from video captured with a stationery camera, separating foreground and background objects and object classification and recognition. The authors identify common challenges faced by researchers including gradual or sudden illumination change, dynamic backgrounds and shadow and ghost regions. This brief concludes with predictions on the future scope of the methods. Clear and concise, this brief equips readers to determine the most effective background subtraction method for a particular project. It is a useful resource for professionals and researchers working in this field.

A new image binarization method using iterative partitioning

Abstract: This paper proposes a new method for image binarization that uses an iterative partitioning approach. The proposed method has been tested towards binarization of both document and graphic images. The quantitative comparisons with other standard methods reveal that the proposed approach outperforms existing widely used binarization techniques in terms of accuracy of binarization. The experimental results further establish the superiority of the proposed method, especially for degraded documents and graphic images. The proposed algorithm is suitable for a multi-core processing environment as it can be split into multiple parallel units of executions after the initial partitioning.

A Comprehensive Survey on Image Binarization Techniques

Abstract: A detailed survey about the principles of image binarization techniques is introduced in this chapter. A comprehensive review is given. A number of classical methodologies together with the recent works are considered for comparison and study of the concept of binarization for both document and graphic images.

Exploring Image Binarization Techniques

Abstract: The book focuses on an image processing technique known as binarization. It provides a comprehensive survey over existing binarization techniques for both document and graphic images. A number of evaluation techniques have been presented for quantitative comparison of different binarization methods. The book provides results obtained comparing a number of standard and widely used binarization algorithms using some standard evaluation metrics. The comparative results presented in tables and charts facilitates understanding the process. In addition to this, the book presents techniques for preparing a reference image which is very much important for quantitative evaluation of the binarization techniques. The results are produced taking image samples from standard image databases.

A Comprehensive Survey of Vision-Based Human Action Recognition Methods.

Abstract: Although widely used in many applications, accurate and efficient human action recognition remains a challenging area of research in the field of computer vision. Most recent surveys have focused on narrow problems such as human action recognition methods using depth data, 3D-skeleton data, still image data, spatiotemporal interest point-based methods, and human walking motion recognition. However, there has been no systematic survey of human action recognition. To this end, we present a thorough review of human action recognition methods and provide a comprehensive overview of recent approaches in human action recognition research, including progress in hand-designed action features in RGB and depth data, current deep learning-based action feature representation methods, advances in human⁻object interaction recognition methods, and the current prominent research topic of action detection methods. Finally, we present several analysis recommendations for researchers. This survey paper provides an essential reference for those interested in further research on human action recognition.

Sensor-based and vision-based human activity recognition: A comprehensive survey

Abstract: Human activity recognition (HAR) technology that analyzes data acquired from various types of sensing devices, including vision sensors and embedded sensors, has motivated the development of various context-aware applications in emerging domains, e.g., the Internet of Things (IoT) and healthcare. Even though a considerable number of HAR surveys and review articles have been conducted previously, the major/overall HAR subject has been ignored, and these studies only focus on particular HAR topics. Therefore, a comprehensive review paper that covers major subjects in HAR is imperative. This survey analyzes the latest state-of-the-art research in HAR in recent years, introduces a classification of HAR methodologies, and shows advantages and weaknesses for methods in each category. Specifically, HAR methods are classified into two main groups, which are sensor-based HAR and vision-based HAR, based on the generated data type. After that, each group is divided into subgroups that perform different procedures, including the data collection, pre-processing methods, feature engineering, and the training process. Moreover, an extensive review regarding the utilization of deep learning in HAR is also conducted. Finally, this paper discusses various challenges in the current HAR topic and offers suggestions for future research.

Abnormal behavior recognition for intelligent video surveillance systems

Abstract: Different levels of an intelligent video surveillance system (IVVS) are studied in this review.Existing approaches for abnormal behavior recognition relative to each level of an IVVS are extensively reviewed.Challenging datasets for IVVS evaluation are presented.Limitations of the abnormal behavior recognition area are discussed. With the increasing number of surveillance cameras in both indoor and outdoor locations, there is a grown demand for an intelligent system that detects abnormal events. Although human action recognition is a highly reached topic in computer vision, abnormal behavior detection is lately attracting more research attention. Indeed, several systems are proposed in order to ensure human safety. In this paper, we are interested in the study of the two main steps composing a video surveillance system which are the behavior representation and the behavior modeling. Techniques related to feature extraction and description for behavior representation are reviewed. Classification methods and frameworks for behavior modeling are also provided. Moreover, available datasets and metrics for performance evaluation are presented. Finally, examples of existing video surveillance systems used in real world are described.

A review of Convolutional-Neural-Network-based action recognition

Abstract: Video action recognition is widely applied in video indexing, intelligent surveillance, multimedia understanding, and other fields. Recently, it was greatly improved by incorporating the learning of deep information using Convolutional Neural Network (CNN). This motivated us to review the notable CNN-based action recognition works. Because CNN is primarily designed to extract 2D spatial features from still image and videos are naturally viewed as 3D spatiotemporal signals, the core issue of extending the CNN from image to video is temporal information exploitation. We divide the solutions for exploiting temporal information exploration into three strategies: 1) 3D CNN; 2) taking the motion-related information as the CNN input; and 3) fusion. In this paper, we present a comprehensive review of the CNN-based action recognition methods according to these strategies. We also discuss the action recognition performance on recent large-scale benchmarks and the limitations and future research directions of CNN-based action recognition. This paper offers an objective and clear review of CNN-based action recognition and provides a guide for future research.