Deep convolutional neural network with Kalman filter based objected tracking and detection in underwater communications

Sreekala, Keshetti; Raj, Nikhil; Gupta, Sachi; Anitha, G.; Nanda, Ashok Kumar; Chaturvedi, Abhay

doi:10.1007/s11276-023-03290-z

Cited by 8 publications

(1 citation statement)

References 28 publications

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“…In another case, the constant torque, which is the linear combination of central torque, has been introduced. Sreekala et al [29] have performed object tracking and detection using a vector including three constant vectors and have used Euclidean distance to match them. The appearance information has been used frequently in object tracking and detection.…”

Section: Introductionmentioning

confidence: 99%

Tracking of moving human in different overlapping cameras using Kalman filter optimized

Yousefi,

Mohseni,

Dehbovid

et al. 2023

EURASIP J. Adv. Signal Process.

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Tracking objects is a crucial problem in image processing and machine vision, involving the representation of position changes of an object and following it in a sequence of video images. Though it has a history in military applications, tracking has become increasingly important since the 1980s due to its wide-ranging applications in different areas. This study focuses on tracking moving objects with human identity and identifying individuals through their appearance, using an Artificial Neural Network (ANN) classification algorithm. The Kalman filter is an important tool in this process, as it can predict the movement trajectory and estimate the position of moving objects. The tracking error is reduced by weighting the filter using a fuzzy logic algorithm for each moving human. After tracking people, they are identified using the features extracted from the histogram of images by ANN. However, there are various challenges in implementing this method, which can be addressed by using Genetic Algorithm (GA) for feature selection. The simulations in this study aim to evaluate the convergence rate and estimation error of the filter. The results show that the proposed method achieves better results than other similar methods in tracking position in three different datasets. Moreover, the proposed method performs 8% better on average than other similar algorithms in night vision, cloud vision, and daylight vision situations.

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Section: Introductionmentioning

confidence: 99%