Susanta Mukhopadhyay

Bio: Susanta Mukhopadhyay is an academic researcher from Indian Institutes of Technology. The author has contributed to research in topics: Encryption & Pixel. The author has an hindex of 9, co-authored 59 publications receiving 337 citations. Previous affiliations of Susanta Mukhopadhyay include Indian Institute of Technology Dhanbad.

Moving object detection based on frame difference and W4

Abstract: Moving object detection is a basic and important task on automated video surveillance systems, because it gives the focus of attention for further examination. Frame differencing and W4 algorithm can be individually employed to detect the moving objects. However, the detected results of the individual approach are not accurate due to foreground aperture and ghosting problems. We propose an approach to segment the moving objects using both the frame differencing and W4 algorithm to overcome the above problems. Here first we compute the difference between consecutive frames using histogram-based frame differencing technique, next W4 algorithm is applied on frame sequences, and subsequently, the outcomes of the frame differencing and W4 algorithm are combined using logical ‘OR’ operation. Finally, morphological operation with connected component labeling is employed to detect the moving objects. The experimental results and performance evaluation on real video datasets demonstrate the effectiveness of our approach in comparison with existing techniques.

A novel method for moving object detection based on block based frame differencing

Abstract: Moving-object detection is one of the basic and most active research domains in the field of computer vision. This paper proposes a novel and efficient approach for moving object detection under a static background. Proposed approach first performs pre-processing tasks to remove noise from video frames. Secondly, it finds the difference between the current frame and previous consecutive frame as well as current frame and next consecutive frame separately. The algorithm then selects the maximum pixel intensity value between both the difference frames, which we consider as an improvement over the previous approaches. Next we divide the resultant difference frame into non-overlapping blocks and calculate the intensity sum and mean of each block. Subsequently, it finds the foreground and background pixels of each block using threshold and intensity mean. In the next step morphology operation along with connected component analysis are applied to correctly detect the target objects. The proposed approach is accurate for detecting the moving object with varying object size and numbers. This work has been formulated, implemented and tested on real video data sets and the results are found to be satisfactory as it evident from the performance analysis.

Motion segmentation-based surveillance video compression using adaptive particle swarm optimization

Abstract: Video surveillance is one of the widely used and most active research applications of computer vision. Although lots of works have been done in the area of smart surveillance, but still there is a need of effective compression technique for compact archival and efficient transmission of vast amount of surveillance video data. In this work, we propose a hybrid video compression approach with the help of foreground motion compensation for the above application. This method works effectively by including the advantages of both block-based and object-based coding techniques as well as reducing the drawbacks of both. The proposed method first segments the foreground moving objects from the background with the help of adaptive thresholding-based optical flow techniques. Next, it determines the contour of the segmented foreground regions with the help of Freeman chain code. Subsequently, block-based motion estimation and compensation using variants of particle swarm optimization are computed. After that, motion failure areas are detected using change detection method, and finally, DCT and Huffman coding-based entropy encoding are done to compactly represent the data. Experimental results and analyses on different surveillance video sequences using Wilcoxon’s rank-sum test, PSNR and SSID show that our method outperforms other recent and relevant existing techniques.

Near-sensor and in-sensor computing

Abstract: The number of nodes typically used in sensory networks is growing rapidly, leading to large amounts of redundant data being exchanged between sensory terminals and computing units. To efficiently process such large amounts of data, and decrease power consumption, it is necessary to develop approaches to computing that operate close to or inside sensory networks, and that can reduce the redundant data movement between sensing and processing units. Here we examine the concept of near-sensor and in-sensor computing in which computation tasks are moved partly to the sensory terminals. We classify functions into low-level and high-level processing, and discuss the implementation of near-sensor and in-sensor computing for different physical sensing systems. We also analyse the existing challenges in the field and provide possible solutions for the hardware implementation of integrated sensing and processing units using advanced manufacturing technologies. This Perspective examines the concept of near-senor and in-sensor computing in which computation tasks are moved partly to the sensory terminals, exploring the challenges facing the field and providing possible solutions for the hardware implementation of integrated sensing and processing units using advanced manufacturing technologies.

Getting to Know Low-light Images with The Exclusively Dark Dataset

Abstract: Low-light is an inescapable element of our daily surroundings that greatly affects the efficiency of our vision. Research works on low-light has seen a steady growth, particularly in the field of image enhancement, but there is still a lack of a go-to database as benchmark. Besides, research fields that may assist us in low-light environments, such as object detection, has glossed over this aspect even though breakthroughs-after-breakthroughs had been achieved in recent years, most noticeably from the lack of low-light data (less than 2% of the total images) in successful public benchmark dataset such as PASCAL VOC, ImageNet, and Microsoft COCO. Thus, we propose the Exclusively Dark dataset to elevate this data drought, consisting exclusively of ten different types of low-light images (i.e. low, ambient, object, single, weak, strong, screen, window, shadow and twilight) captured in visible light only with image and object level annotations. Moreover, we share insightful findings in regards to the effects of low-light on the object detection task by analyzing visualizations of both hand-crafted and learned features. Most importantly, we found that the effects of low-light reaches far deeper into the features than can be solved by simple "illumination invariance'". It is our hope that this analysis and the Exclusively Dark dataset can encourage the growth in low-light domain researches on different fields. The Exclusively Dark dataset with its annotation is available at this https URL

A novel Black Widow Optimization algorithm for multilevel thresholding image segmentation

Abstract: Segmentation is a crucial step in image processing applications. This process separates pixels of the image into multiple classes that permits the analysis of the objects contained in the scene. Multilevel thresholding is a method that easily performs this task, the problem is to find the best set of thresholds that properly segment each image. Techniques as Otsu’s between class variance or Kapur’s entropy helps to find the best thresholds but they are computationally expensive for more than two thresholds. To overcome such problem this paper introduces the use of the novel meta-heuristic algorithm called Black Widow Optimization (BWO) to find the best threshold configuration using Otsu or Kapur as objective function. To evaluate the performance and effectiveness of the BWO-based method, it has been considered the use of a variety of benchmark images, and compared against six well-known meta-heuristic algorithms including; the Gray Wolf Optimization (GWO), Moth Flame Optimization (MFO), Whale Optimization Algorithm (WOA), Sine–Cosine Algorithm (SCA), Slap Swarm Algorithm (SSA), and Equilibrium Optimization (EO). The experimental results have revealed that the proposed BWO-based method outperform the competitor algorithms in terms of the fitness values as well as the others performance measures such as PSNR, SSIM and FSIM. The statistical analysis manifests that the BWO-based method achieves efficient and reliable results in comparison with the other methods. Therefore, BWO-based method was found to be most promising for multi-level image segmentation problem over other segmentation approaches that are currently used in the literature.