Amlan Raychaudhuri

Bio: Amlan Raychaudhuri is an academic researcher from B. P. Poddar Institute of Management & Technology. The author has contributed to research in topics: Block (data storage) & Change detection. The author has an hindex of 1, co-authored 3 publications receiving 3 citations.

Detection of Moving Objects in Video Using Block-Based Approach

Abstract: In this paper, an efficient technique has been proposed to detect moving objects in the video under dynamic as well as static background condition. The proposed method consists block-based background modelling, current frame updating, block processing of updated current frame and elimination of background using bin histogram approach. Next, enhanced foreground objects are obtained in the post-processing stage using morphological operations. The proposed approach effectively minimizes the effect of dynamic background to extract the foreground information. We have applied our proposed technique on Change Detection CDW-2012 dataset and compared the results with the other state-of-the-art methods. The experimental results prove the efficiency of the proposed approach compared to the other state-of-the-art methods in terms of different evaluation metrics.

Moving Object Detection in Video Under Dynamic Background Condition Using Block-Based Statistical Features

Abstract: In this paper, an efficient technique has been proposed to detect moving objects in the video under dynamic background condition. The proposed method consists block-based background modeling, block processing of current frame and elimination of background using bin histogram approach. Next, enhanced foreground objects are obtained in the post-processing stage using morphological operations. The proposed approach effectively minimizes the effect of dynamic background to extract the foreground information. We have applied our proposed technique on Change Detection CDW-2012 dataset and compared the results with the other state-of-the-art methods. The experimental results prove the efficiency of the proposed approach compared to the other state-of-the-art methods in terms of different evaluation metrics.

A novel approach for human silhouette extraction from video data

Abstract: In this paper we propose a method for efficient extraction of human silhouette from video sequences. The proposed approach includes background elimination, edge detection, region filling and noise removal using morphological operations to estimate the silhouette of an image. To the best of our knowledge our proposed approach for silhouette extraction involving background elimination and edge detection is first of its kind. We have applied our proposed technique on Weizmann (standard) dataset and compared the results with the most recent related research work. The comparison results in terms of statistical measures like precision, recall and F-measure clearly show the supremacy of our method and thus justify its novelty.

Moving Object Detection Based on Background Compensation and Deep Learning

Abstract: Detecting moving objects in a video sequence is an important problem in many vision-based applications. In particular, detecting moving objects when the camera is moving is a difficult problem. In this study, we propose a symmetric method for detecting moving objects in the presence of a dynamic background. First, a background compensation method is used to detect the proposed region of motion. Next, in order to accurately locate the moving objects, we propose a convolutional neural network-based method called YOLOv3-SOD for detecting all objects in the image, which is lightweight and specifically designed for small objects. Finally, the moving objects are determined by fusing the results obtained by motion detection and object detection. Missed detections are recalled according to the temporal and spatial information in adjacent frames. A dataset is not currently available specifically for moving object detection and recognition, and thus, we have released the MDR105 dataset comprising three classes with 105 videos. Our experiments demonstrated that the proposed algorithm can accurately detect moving objects in various scenarios with good overall performance.

An Efficient Approach for Detecting and Classifying Moving Vehicles in a Video Based Monitoring System

Abstract: Object detection, object recognition, background subtraction, image processing, traffic analysis. Moving objects detection, type recognition, and traffic analysis in videobased surveillance systems is an active area of research which has many applications in road traffic monitoring. This paper is on using classical approaches of image processing to develop an efficient algorithm for computer vision based on traffic surveillance system that can detect and classify moving vehicles, besides serving some other traffic analysis issues like finding vehicles speed and heading, tracking specified vehicles, and finding traffic load. The algorithm is designed to be flexible for modification to fulfill the changes in design objectives, having limited computation time, giving good accuracy, and serves inexpensive implementation. A 92% of success is achieved for the considered test, with the missed cases being abnormal that are not defined to the algorithm. The computation time, with a platform (hardware and software) dependent, the algorithm took to produce results was parts of milliseconds. A CNN based deep learning classifier was built and evaluated to judge the feasibility of involving a modern approach in the design for the targeted aims in this work. The modern NN based deep learning approach is very powerful and represents the choice for many very sophisticated applications, but when the purpose is restricted to limited requirements, as it is believed the case is here, the reason will be to use the classical image processing procedures. In making choice, it is important to consider, among many things, accuracy, computation time, and simplicity of design, development, and implementation. How to cite this article: S. S. Mahmood, L. J. Saud. “An efficient approach for detecting and classifying moving vehicles in a video based monitoring system,” Engineering and Technology Journal, Vol. 38, No. 06, pp. 832-845, 2020. DOI: https://doi.org/10.30684/etj.v38i6A.438 Engineering and Technology Journal Vol. 38, Part A, (2020), No. 60, Pages 832-845 833

Detection of Moving Objects in Video Using Block-Based Approach

Abstract: In this paper, an efficient technique has been proposed to detect moving objects in the video under dynamic as well as static background condition. The proposed method consists block-based background modelling, current frame updating, block processing of updated current frame and elimination of background using bin histogram approach. Next, enhanced foreground objects are obtained in the post-processing stage using morphological operations. The proposed approach effectively minimizes the effect of dynamic background to extract the foreground information. We have applied our proposed technique on Change Detection CDW-2012 dataset and compared the results with the other state-of-the-art methods. The experimental results prove the efficiency of the proposed approach compared to the other state-of-the-art methods in terms of different evaluation metrics.

A Fusion of Dolphin Swarm Optimization and Improved Sine Cosine Algorithm for Automatic Detection and Classification of Objects from Surveillance Videos

Abstract: Surveillance is the utmost extensively used technology in the current scenario. In real-time, it is immensely applicable in all domains to monitor, identifying the moving objects, and tracking through computer vision. The object detection and classification is an important process in surveillance video. During this task, the visual appearance will change according to the viewing angles, lightening, and distance from the camera. It is necessary to improve the efficiency in real-time detection and classification of objects from surveillance videos. To obtain this, we proposed a fusion of Dolphin Swarm Optimization (DSO) and improved Sine Cosine algorithm (ISCA) based Support Vector Machine (SVM) classifier which includes the following steps: Frame differencing for foreground segmentation, Histogram of Oriented Gradients (HOG) for feature extraction, and DSO-ISCA-SVM classifier for classification. Initially, the surveillance videos are collected and the acquisition of images from the surveillance video camera. Secondly, the moving objects are detected by frame differencing in which the difference between two frames are estimated and compared with the threshold value. Then the shadow and noise are removed. Thirdly, the HOG capture local shapes through gradients. Finally, the proposed DSO-ISCA-SVM classifier accurately classifies the objects from the surveillance video, the DSO-ISCA is used to find the SVM parameters. This proposed technique effectively detects and classify the objects from the surveillance videos. The proposed technique results are compared with other existing methods. The experimental results prove that the proposed method efficiency is better than the existing methods in terms of different evaluation metrics.

Human Fall Down Recognition Using Coordinates Key Points Skeleton

Abstract: Falls pose a substantial threat to human safety and can quickly result in disastrous repercussions. This threat is particularly true for the elderly, where falls are the leading cause of hospitalization and injury-related death. A fall that is detected and responded to quickly has a lower danger and long-term impact. Many real-time fall detection solutions are available; however, these solutions have specific privacy, maintenance, and proper use issues. Vision-based fall event detection has the benefit of being completely private and straightforward to use and maintain. However, in real-world scenarios, falls are diverse and result in high detection instability. This study proposes a novel vision-based technique for fall detection and analyzes an extracted skeleton to define human postures. OpenPose can be used to get skeletal information about the human body. It identifies a fall using three critical parameters: the center of the value of the head and shoulder coordinates, the critical points of the shoulder coordinates, and the distance between the center of the skeleton's head and the floor with the angle between the center of the shoulders and the ground. Our proposed methodology was effective, with a classification accuracy of 97.7%.