An efficient moving object detection and tracking system based on fractional derivative

Lingaswamy, Sindhia; Kumar, David D.

doi:10.1007/s11042-018-5843-6

Cited by 4 publications

(2 citation statements)

References 13 publications

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“…So using fractional order derivatives should increase accuracy of predicting the next position of tracked object. A moving object detection and tracking system based on fractional derivative was presented in [10] Authors proposed that fractional order derivative is calculated on the subsequent image which are later inputted to Otsu's threshold system to complete segmentation. Fractional calculus was also used in order to improve the accuracy of tracking methods by estimating the position of football players based on their movement trajectory [11].…”

Section: Related Workmentioning

confidence: 99%

Fractional Order Derivatives as an Optimization Tool for Object Detection and Tracking Algorithms

et al. 2022

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Optimization techniques are nowadays one of the most important branches of computer science due to the limitations of the computing power availability especially in cases of mobile platforms. Algorithms which can be used on such units have to be optimized for memory and computing power. In the paper we focused on optimization techniques for image analysis algorithms by limiting the number of necessary operations. The optimized algorithm has been designed to detect and follow a specified marker, known a priori, and is based on the correlation coefficient match between the acquired template and the current image. The acquired template is taken from the previous processed frame. The match operation is based on Pearson's correlation coefficient, so the whole mechanism is therefore highly demanding in terms of computing power. Optimization is performed mainly using the Region of Interest (ROI) to exclude irrelevant parts of the image. The algorithm is optimized using Grünvald -Letnikov fractional -order backward difference to estimate the position of the marker in a sequence of images. This limits the number of operations required to maintain the precision of the algorithm. Based on the position of the object in previous frames, a fractional order mathematical tool is able to estimate at relatively low cost and with high accuracy the probable position of the object in the incoming image. Here, we explain the workflow of the template detection and following algorithm, as well as the mathematical basis of the fractional order derivative optimization tool. The processor load connected to the optimized algorithm was reduced by over 35%.INDEX TERMS autonomous mobile robots, fractional order difference, template tracking, prediction of moving object position

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Section: Related Workmentioning

confidence: 99%

Fractional Order Derivatives as an Optimization Tool for Object Detection and Tracking Algorithms

et al. 2022

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“…Thereafter, the extracted features are passed to the Genetic algorithm and Random Forest classifier for feature reduction and classification purposes. Sindhia and Dhananjay [17] used the fractional derivatives technique to build an efficient object detection and tracking system. To detect the moving object, the approach adopted the forward and backward tracking concept in which fractional derivative is figured out for each pre-processed frame.…”

Section: Related Workmentioning

confidence: 99%

An expert video surveillance system to identify and mitigate shoplifting in megastores

Ansari

Singh

2021

Multimed Tools Appl

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Shoplifting has got serious concern because of a steep surge in these types of cases all around. People are found stealing the items from the store without being noticed, either by putting them in bags or hiding objects inside clothes. CCTV cameras are generally installed at any such site, but evidences suggest that these cameras are not very effective unless the video feeds are constantly monitored. Therefore, we intend to build an automated and intelligent surveillance system to catch these shoplifters by identifying their stealing actions. This article proposes a deep neural network-based solution to identify these shoplifting activities. The model proposed uses a dual-stream fusion-based network that effectively binds appearance and motion dynamics in the temporal domain to efficiently identify the shoplifting actions. The deep Inception V3 model is used to extract activity-specific body posture features from video streams through two deep neural network pipelines, one each corresponding to appearance and motion information. Next, a recurrent neural network, namely Long Short Term Memory (LSTM) network, is used to build a temporal relation between features extracted from consecutive frames in order to distinguish human stealing actions accurately. Added to it, this article introduces a shoplifting dataset synthesized in our lab, which contains normal human actions and object stealing actions. The proposed methodology supported with experimental results demonstrates encouraging outcomes with the accuracy achieved up to 91.48%, which outperforms other existing methods.

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Applications of fractional calculus in computer vision: A survey

et al. 2022

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An efficient moving object detection and tracking system based on fractional derivative

Cited by 4 publications

References 13 publications

Fractional Order Derivatives as an Optimization Tool for Object Detection and Tracking Algorithms

Fractional Order Derivatives as an Optimization Tool for Object Detection and Tracking Algorithms

An expert video surveillance system to identify and mitigate shoplifting in megastores

Applications of fractional calculus in computer vision: A survey

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