Achieving subpixel accuracy in object tracking presents significant advantages for motion and deformation analysis. While accuracies exceeding 0.01 pixels are attainable under optimal conditions, sustaining these conditions is often limited to short durations. The heating of devices can induce sensor and housing expansion, resulting in image distortions. In this study, we investigate thermal effects by capturing static sequences of a binary target comprising a matrix of circles. Images were captured every two minutes over a 15-hour period. Subpixel tracking of image drifts and deformations was achieved by locating the centroid of each circle. We evaluated the performance of a Basler Ace2 camera both in its standard configuration and with a heat sink accessory, demonstrating the effectiveness of the heat sink in reducing stabilization time and minimizing drift and distortions. Our findings indicate that while incorporating a heat sink offers advantages, potential drawbacks must also be considered.
The design and adjustment of image-based detection and tracking systems require the use of calibrated sliders on which to place the targets. Scientific-grade sliders often provide high precision and repeatability (1 micron), although they are frequently expensive and can only move small-size and lightweight targets, limiting their applications.
In our research work, we need to conduct motion detection tests with subpixel precision by tracking natural textures on stone materials. The test samples used in the laboratory can weight more than 1 kg to have a statistically representative amount of texture details. This weight makes them too heavy for scientific sliders.
In this work, we propose the use of photographic sliders as precision displacement systems. Since these systems are designed for artistic purposes, precise calibration is necessary for their use in accurate displacements. The calibration procedure involves the precise tracking of a circular target’s centroid location with subpixel accuracy. To achieve this, the slider’s speed and the camera’s acquisition time are adjusted, ensuring that interframe is around 0,1 pixels. The same trajectory is assessed at various displacement speed to determine both the repeatability and linearity in velocity and positioning.
The tests were conducted using a Thorlabs DDS100/M linear slider and an Edelkrone One photographic slider. The results demonstrate that the photographic slider delivers a similar level of precision at just one-fifth of the cost when compared to the Thorlabs slider.
Object tracking with subpixel accuracy is crucial in experiments where the object’s apparent movement on the camera sensor is very small. This situation can occur when the movement is inherently minimal or when it takes place at a significant distance from the camera. Achieving accuracies beyond 0.01 pixels requires careful planning and noise cancellation to obtain precise and consistent results. Therefore, it is imperative to meticulously design experiments in the laboratory to determine the true performance under the best possible conditions.
To achieve high subpixel accuracy, it is necessary to find a balance among the camera's features, the object to camera distance, and the object’s speed. These parameters collectively define the final pixel-to-millimeter ratio, which ultimately determines the method’s accuracy.
Additionally, selecting the appropriate algorithm is fundamental for accurately determining the target’s position. In our case, we employ normalized cross-correlation between images with analytic interpolation of the correlation peak.
A drawback of subpixel tracking approach is that tracking targets with subpixel accuracy makes the system highly sensitive to thermal errors. Heating of the electronics can lead to the expansion of the camera casing and sensor, resulting in drifts and distortions in the final image.
In our presentation, we will show different combinations that ensure precise subpixel accuracy while accounting for observed thermal distortions. Following our results with Basler cameras, our recommendation is to use the lowest target speed with a temporal resolution to achieve an apparent interframe shift of less than 0.004 pixels and at least 2 hours of stabilization time.
Some materials undergo an hygric expansion when they are soaked. In porous rocks, this effect is enhanced by the pore space that allows the water to reach every part of its volume and to hydrate the most of their swelling parts. This enlargement has negative structural consequences in the vicinity since adjacent elements will support some compressions or displacements. Recently image-based methods have arisen in this field due to their advantages versus traditional methods. Among all image processing methods, digital image correlation (DIC) is one of the most used in all areas. In this work, we propose a new methodology based on DIC for the calculation of the hygric expansion of materials. We use porous sandstone, with dimensions 14x14x30 mm to measure its hygric swelling using an industrial digital camera and a telecentric objective. We took one image every 5 minutes to characterize the whole swelling process. Due the large magnification, the whole 14 mm length of one contour was not in the image and therefore we lost the image scale reference. To solve this, a 1951 USAF test was used to calibrate the imagen. The telecentric objective and a narrow deep of field allowed to have the specimen surface exactly on the same plane that the USAF test was during the calibration. The image was pointed to one corner of the specimen, to obtain information not only of its vertical displacement due to its expansion but also of its horizontal movement. Preliminary results show that the proposed methodology provides reliable information of the hygric swelling using a non-contact methodology, with an accuracy of 1 micron.
A technique for obtaining subpixel resolution when tracking through cross-correlation consists of interpolating the obtained function and then refine the peak location. Although the technique provides accurate location results, the peak is always biased towards the closest integer coordinate. This effect is known as peak-locking error and is a major limit to the experimental accuracy of this calculation technique. This error may be different depending on the algorithm used to fit and interpolate the correlation peak but no systematic analysis was found in the literature. In our study we explore the three most common interpolation methods: thin-plate splines, second-order polynomial fit and Gaussian fit together with the influence of the extent of local interpolation area around the peak. Additionally, we have checked the influence of the image blurring on the results, since it is reported as one effective method to reduce the peak-locking error. Finally, the optimal adjustment found is the Gaussian fit with no blur and a neighborhood around the correlation peak of 11x11 pixels.
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