&lt;title&gt;Centroid tracker and aimpoint selection&lt;/title&gt;

Venkateswarlu, Ronda; Sujata, K. V.; Rao, B. Venkateswara

doi:10.1117/12.138205

Cited by 9 publications

(5 citation statements)

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“…These low-frequency regions are avoided in the ltering process. AONF uses the hot point detection process of [30] to identify the noisy peaks. Once the noisy peaks are identi ed, the algorithm uses the traditional region-growing algorithm to quantify the noisy region to design the Gaussian notch lter for denoising the noisy regions.…”

Section: Discussion Analysis and Comparisonmentioning

confidence: 99%

Frequency-domain-based switching median filter for the restoration of images corrupted with high-density periodic noise

Varghese

2017

Scientia Iranica

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Abstract. The paper proposes an adaptive Frequency-Domain-based Switching MedianFilter (FDSMF) for the restoration of images corrupted by periodic noise. The proposed algorithm incorporates region-growing technique to e ectively identify noisy peak areas of the Fourier transformed image in a binary noise map image. The restoration phase of the algorithm replaces the corrupted frequencies with the median of uncorrupted frequencies by recursive median lter. Experimental results from di erent naturally and arti cially corrupted images at various noise levels/types reveal that the performance of the proposed algorithm in restoring images corrupted by periodic noise is better than other competing algorithms in terms of subjective and objective metrics.

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Section: Discussion Analysis and Comparisonmentioning

confidence: 99%

Frequency-domain-based switching median filter for the restoration of images corrupted with high-density periodic noise

Varghese

2017

Scientia Iranica

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“…This seemingly simple problem becomes extremely challenging because of practical issues such as the orientation of the target, occlusions, movement of the target and related scale variations. Earlier tracking algorithms [13] used the centroid of the image as a reference parameter to predict the position of the target in the next frame. In this method, the target is assumed to move very slowly between consecutive frames.…”

Section: Classical Video Trackingmentioning

confidence: 99%

“…Centroid processing [13] and correlation processing [14] are third-generation automated video tracking techniques for slow-moving targets mentioned in categories (a) and (b) above. Centroid processing requires initial selection of the target bounding box which is done manually to start the track.…”

Section: Introductionmentioning

confidence: 99%

UAV-based autonomous detection and tracking of beyond visual range (BVR) non-stationary targets using deep learning

Chandrakanth¹,

Murthy²,

Channappayya³

2021

J Real-Time Image Proc

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Aerial surveillance and tracking have gained significant traction in recent years for both civilian applications and military reconnaissance. Disaster analysis, emergency medical response, pandemic spread analysis, etc. have significantly improved with the availability of aerial data. The next big step is to push the system for autonomous detection and tracking of targets beyond visual range (BVR). Presently, this is done using GPS-based techniques in which the target information is assumed to be precisely known. In situations where such information is unavailable or if the target of interest is non-stationary, this method is not applicable and currently, no alternative exists. In this work, we aim to address this limitation and propose a deep learning-based algorithm for terminal guidance of aerial vehicle BVR with only bearing information about the target of interest. The algorithm operates in search and track modes. We describe both the modes and also discuss the challenges associated with this kind of deployment in real time. Since the weight and power requirements of the payload directly translate to the cost of deployment and endurance of aerial vehicles, we have configured a custom lightweight convolutional neural network (CNN) with minimal layers and successfully deployed the system on Jetson Nano, the smallest GPU available from NVIDIA as of this writing. We evaluated the performance of the proposed algorithm on proprietary and open-source datasets and achieved detection accuracy greater than 98.6% on custom datasets.

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“…For solidified targets like tanks, appropriate aimpoint determination and direction up to right around zero territory is basic to accomplish maximum kill probability. [4] Understanding the movement of object in a video rises an interest due to its increased automation in CCTV, controlling control and also the analysis of movements of pedestrians. In this paper, we are presenting a system which is further used for detection and differentiation of people and vehicles in a variety of different weather conditions upon using a static camera.…”

Section: Literature Reviewmentioning

confidence: 99%

Face Recognition using Cascade Algorithm

2019

ijrte

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As one of the most successful application of Image processing, face recognition has received attention for quite a long time. Whether being used in CCTV cameras or home security, face recognition is an important application in today’s metropolitan era. The face recognization strategy is implemented by using Haar cascade algorithm. Both are used differently and the results are then compared to know which one works better or is more accurate. In this paper, we have used Python programing language, since this is only a basic Face recognition system there is no database used but for future extension, we can add a database to expand this method to a larger scale.

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<title>Centroid tracker and aimpoint selection</title>

Cited by 9 publications

References 0 publications

Frequency-domain-based switching median filter for the restoration of images corrupted with high-density periodic noise

Frequency-domain-based switching median filter for the restoration of images corrupted with high-density periodic noise

UAV-based autonomous detection and tracking of beyond visual range (BVR) non-stationary targets using deep learning

Face Recognition using Cascade Algorithm

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