ArticleHeliyon2023
A fuzzy decision-making system for video tracking with multiple objects in non-stationary conditions.
Article in Heliyon, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It has been retracted, and should not be counted. Cited by 5 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
5 citing papers in PubMed.
- Efficient hybrid fuzzy weighted 3D FCNN with TSO PSO optimization for accurate multi modal MRI brain tumor classification.Scientific reports · 2025Article
- Adaptive temporal attention mechanism and hybrid deep CNN model for wearable sensor-based human activity recognition.Scientific reports · 2025Article
- Combination of Deep and Statistical Features of the Tissue of Pathology Images to Classify and Diagnose the Degree of Malignancy of Prostate Cancer.Journal of imaging informatics in medicine · 2025Article
- Detection and isolation of brain tumors in cancer patients using neural network techniques in MRI images.Scientific reports · 2024Article
- Article
Corrections and comments
- Retraction · 2025-10-03Concerns/Issues about Referencing/Attributions · Investigation by Journal/Publisher · Objections by Author(s) · Unreliable Results and/or Conclusions ·
- Retracted
Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Computer vision remains challenged by tracking multiple objects in motion frames, despite efforts to improve surveillance, healthcare, and human-machine interaction. This paper presents a method for monitoring several moving objects in non-stationary settings for autonomous navigation. Additionally, at each phase, movement information between successive frames, including the new frame and the previous frame, is employed to determine the location of moving objects inside the camera's field of view, and the background in the new frame is determined. With the help of a matching algorithm, the Kanade-Lucas-Tomasi (KLT) feature tracker for each frame is determined. To get the new frame, we access the matching feature points between two subsequent frames, calculate the movement size of the feature points and the camera movement, and subtract the previous frame of moving objects from the current frame. Every moving object within the camera's field of view is captured at every moment and location. The moving items are categorized and segregated using fuzzy logic based on their mass center and length-to-width ratio. Our algorithm was implemented to investigate autonomous navigation surveillance of three types of moving objects, such as a vehicle, a pedestrian, a bicycle, or a motorcycle. The results indicate high accuracy and an acceptable time requirement for monitoring moving objects. It has a tracking and classification accuracy of around 75 % and processes 43 frames per second, making it superior to existing approaches in terms of speed and accuracy.
Indexed as
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What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.