Symmetric Mask Wavelet Based Detection and Tracking of Moving Objects Using Variance Method

Resmi, H.B.; Deepambika, V.A.; Rahman, M.

doi:10.1016/j.procs.2015.08.012

Cited by 7 publications

(2 citation statements)

References 6 publications

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“…The increase in the level of decomposition used to meet the low memory requirements often ends in incomplete object detection. The symmetric mask-based discrete wavelet transform (SMDWT) is an apt choice to meet these demands, as it is always associated with attractive characteristics such as shorter path, independent subband coding, and fast computation [11,12]. Generally, these approaches are done on monocular videos and it often face challenges such as multiple object occlusion, shadow interference, and radiometric changes.…”

Section: Introductionmentioning

confidence: 99%

Illumination Invariant Motion Detection and Tracking Using SMDWT and a Dense Disparity-Variance Method

Deepambika

Rahiman

2018

Journal of Sensors

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The navigation management systems in autonomous vehicles should be able to gather solid information about the immediate environment of the vehicle, discern ambulance from a delivery truck, and react in a proper manner to handle any difficult situation. Separating such information from a vision controlled system is a computationally demanding task for heavy traffic areas in the real world environmental conditions. In such a scenario, we need a robust moving object detection tracking system. To achieve this, we can make use of stereo vision-based moving object detection and tracking, utilizing symmetric mask-based discrete wavelet transform to deal with illumination changes, low memory requirement, and fake motion avoidance. The accurate motion detection in complex dynamic scenes is done by the combined background subtraction and frame differencing technique. For the fast motion track, we can employ a dense disparity-variance method. This SMDWT-based object detection has a maximum and minimum accuracy of 99.62% and 94.95%, respectively. The motion track has the highest accuracy of 79.47% within the time frame of 28.03 seconds. The lowest accuracy of the system is 62.01% within the time frame of 34.46 seconds. From the analysis, it is clear that this proposed method exceptionally outperforms the existing monocular and dense stereo object tracking approaches in terms of low computational cost, high accuracy, and in handling the dynamic environments.

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Section: Introductionmentioning

confidence: 99%

Illumination Invariant Motion Detection and Tracking Using SMDWT and a Dense Disparity-Variance Method

Deepambika

Rahiman

2018

Journal of Sensors

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“…It could give a more exact frontal area/foundation partition, contrasted with established tracking techniques which frequently utilize a bouncing box to characterize the objective question [11]. Despite the fact that similarly object detection and tracking is done utilizing different apparatus [12]. And also tracking the deformable object in reasonable situations is still challenging in light of the fact that the objective appearance may vary continually amid moving.…”

Section: Introductionmentioning

confidence: 99%

Similar Object Detection and Tracking in H.264 Compressed Video Using Modified Local Self Similarity Descriptor and Particle Filtering

Kuppuswamy¹,

Panchanathan²

2017

IJIES

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Abstract:Object tracking is a dynamic optimization process based on the temporal information related to the previous frames. Proposing a method with higher precision in complex environments is a challenge for researchers in the field of study. In this paper, we have proposed a novel framework for similar object tracking. In our proposed technique we are considering both PETS and Cricket video for input video sequence. The primary steps of suggested technique are preprocessing, background subtraction and segmentation, similar object detection and object tracking. In the preprocessing stage, the adaptive median filter is used to remove the noise from each frame. Next, the foreground and background images are separated and then segmentation of object is carried out by using morphological operation. For similar object detection, the recommended technique uses the modified local self-similarity descriptor and similar object tracking is done by a particle filter. The performance of the suggested technique is evaluated by means of precision, recall, F-measure, FPR, FNR, PWC, FAR, similarity, specificity, and accuracy. An experimental result shows that the proposed technique attains the maximum tracking efficiency for both videos when compared to the existing techniques.

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Video Annotation and Player Classification Using Hough-Grid Transformation (HGT) Method

Makandar

Mulimani

2019

Communications in Computer and Information Science

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Symmetric Mask Wavelet Based Detection and Tracking of Moving Objects Using Variance Method

Cited by 7 publications

References 6 publications

Illumination Invariant Motion Detection and Tracking Using SMDWT and a Dense Disparity-Variance Method

Illumination Invariant Motion Detection and Tracking Using SMDWT and a Dense Disparity-Variance Method

Similar Object Detection and Tracking in H.264 Compressed Video Using Modified Local Self Similarity Descriptor and Particle Filtering

Video Annotation and Player Classification Using Hough-Grid Transformation (HGT) Method

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