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- BildiriYalnız künye2026Analysis of Temporal Consistency in Video-Based Palmprint ROI Tracking
Sunnetci, Betul Kosmaz; Bingol, Ozkan; Gedikli, Eyup
In real-time palmprint recognition systems, operating under unconstrained conditions, maintaining the defined palmprint region of interest (ROI) throughout hand motion presents a significant challenge. Variations in hand position or orientation may cause the ROI to shift unexpectedly or partially or completely disappear, which can adversely affect subsequent processing steps and their computational cost. In this study, the temporal stability and consistency of palmprint ROIs extracted from video data in the MPW-180 dataset were evaluated using two different approaches. Specifically, the behavior of two methods (optical flow based tracking and keypoint-based MediaPipe Hands detection) was quantitatively analyzed over time using corner position shifts and overlap ratios. Experimental results indicate that although the keypointbased method produces spatially accurate ROI estimates, it may exhibit sudden positional jumps in certain frames. In contrast, the optical flow method provides smoother motion transitions but may introduce positional drift over time due to the accumulation of tracking errors.
- BildiriAçık Erişim2024In Different Scenarios MobileNet-V1 for Cross-view Gait Recognition
Yaprak, Busranur; Gedikli, Eyup; Badica, C.; Ivanovic, M.; Koprinkova-Hristova, P.; Leon, F.; Manolopoulos, Y.; Yildirim, T.; Ucar, A.
Gait recognition aims to identify people from a distance by analyzing their walking style. Nevertheless, the efficacy of recognition drops significantly under cross-view and, appearance-based variations such as carrying and clothing. In this study, the performance of the MobileNet-V1 deep network is evaluated in various scenarios to address the cross-view gait recognition problem. In the first scenario, the fine-tuned MobileNet-V1 is evaluated on Gait Energy Images (GEI) as input data, while in the second scenario, the fine-tuned MobileNet-V1 is assessed with Optical Flows and masked RGB frames input data. In the last scenario, the first two scenarios are combined over a single fused deep network based on finetuned MobileNet-V1, and a single recognition process is performed using two different fused features data; GEI features with Optical Flow features, and GEI features with masked RGB frame features. In the evaluation process, a comprehensive data set for the cross-view gait recognition problem, CASIA-B is used for the experiments. The obtained results demonstrate that in the last scenario, the contribution of masked RGB frame features to the recognition rate of GEI is more significant.
- BildiriYalnız künye2017Observing the Entropy of Crowd Movements
Akpulat, Murat; Ekinci, Murat
Providing security and managing it as well as trying to understand how it behaves have become an important research topic in our day. The aim of this study is to calculate the entropy value of the crowd movement regionally and to track how it. changes in time. Because, excessive changes in the entropy value in time might point to abnormal behaviour in the region. The motion information obtained from the optical flow method from the image in parallel to the target was converted to motion orbits by particle advection. Motion orbits are mapped into by the Braid Theory and entropy value is calculated. Braid Theory is a mathematical method that is used to express entropy as a qualitative value by determining the interaction among the movement orbits. The results are tested according to the video sequence in the PETS2009 and UCF database and they show that this method is functional for observing how the entropy changes in time within the crowd movement.
- BildiriYalnız künye2016Detecting and Classifying Dominant Crowd Movements through Particle Advection
Akpulat, Murat; Ekinci, Murat
Today, trying to understand what kind of behaviour the crowd shows by studying the data from surveillance systems is an important topic for researchers of computer vision. The aim of this study make the motion data that is at pixel level and that is obtained by optical flow method a more meaningful data set with the particle advection method. In other words, the aim is to monitor the motion data by converting the 3D optical flow data set to a 2D data set. With this respect, evaluating how the crowd motion changes throughout the video by focusing only on the moments where there is motion can be carried out easily. At the end of the study, the success of the method is presented by testing how the image acts as a dominant movement.
