High dynamic range imaging implementation in scene monitoring under bad illumination

Unapređenje kvaliteta slike širenjem dinamičkog opsega u poslednje vreme se intenzivno koristi. Ovo za posledicu ima prisustvo znatno više detalja na slici, što je jako bitno u većini primena. Širenje dinamičkog opsega ima svoje granice i one su definisane fizičkim limitima senzora koji se koristi, tj. ograničenjima njegovog A/D konvertora. Kada je dinamički opseg scene značajno širi od dinamičkog opsega senzora, mnogi detalji neće biti adekvatno prikazani na slici. Međutim, ukoliko senzor inherentno podržava široki dinamički opseg, jasno se može uočiti da je snimljena slika kvalitetnija od one koja se dobija sa standardnog senzora...Improving image quality by expanding the dynamic range is extensively used recently. This results in the presence of significantly more details in the picture, which is very important for most applications. Expanding the dynamic range has its limits, and they are defined by the physical limits of sensor used, i.e. the limits of its A / D converter. When the dynamic range of the scene is significantly wider than the dynamic range of the sensor, many details will not be shown properly in the picture. However, if the sensor inherently supports wide dynamic range, it can be clearly noticed that the recorded image quality is higher than the one obtained with the standard sensors..

High dynamic range imaging implementation in scene monitoring under bad illumination

Unapređenje kvaliteta slike širenjem dinamičkog opsega u poslednje vreme se intenzivno koristi. Ovo za posledicu ima prisustvo znatno više detalja na slici, što je jako bitno u većini primena. Širenje dinamičkog opsega ima svoje granice i one su definisane fizičkim limitima senzora koji se koristi, tj. ograničenjima njegovog A/D konvertora. Kada je dinamički opseg scene značajno širi od dinamičkog opsega senzora, mnogi detalji neće biti adekvatno prikazani na slici. Međutim, ukoliko senzor inherentno podržava široki dinamički opseg, jasno se može uočiti da je snimljena slika kvalitetnija od one koja se dobija sa standardnog senzora...Improving image quality by expanding the dynamic range is extensively used recently. This results in the presence of significantly more details in the picture, which is very important for most applications. Expanding the dynamic range has its limits, and they are defined by the physical limits of sensor used, i.e. the limits of its A / D converter. When the dynamic range of the scene is significantly wider than the dynamic range of the sensor, many details will not be shown properly in the picture. However, if the sensor inherently supports wide dynamic range, it can be clearly noticed that the recorded image quality is higher than the one obtained with the standard sensors..

High dynamic range imaging implementation in scene monitoring under bad illumination

Unapređenje kvaliteta slike širenjem dinamičkog opsega u poslednje vreme se intenzivno koristi. Ovo za posledicu ima prisustvo znatno više detalja na slici, što je jako bitno u većini primena. Širenje dinamičkog opsega ima svoje granice i one su definisane fizičkim limitima senzora koji se koristi, tj. ograničenjima njegovog A/D konvertora. Kada je dinamički opseg scene značajno širi od dinamičkog opsega senzora, mnogi detalji neće biti adekvatno prikazani na slici. Međutim, ukoliko senzor inherentno podržava široki dinamički opseg, jasno se može uočiti da je snimljena slika kvalitetnija od one koja se dobija sa standardnog senzora...Improving image quality by expanding the dynamic range is extensively used recently. This results in the presence of significantly more details in the picture, which is very important for most applications. Expanding the dynamic range has its limits, and they are defined by the physical limits of sensor used, i.e. the limits of its A / D converter. When the dynamic range of the scene is significantly wider than the dynamic range of the sensor, many details will not be shown properly in the picture. However, if the sensor inherently supports wide dynamic range, it can be clearly noticed that the recorded image quality is higher than the one obtained with the standard sensors..

Gyroscope-Based Video Stabilization for Electro-Optical Long-Range Surveillance Systems

Video stabilization is essential for long-range electro-optical systems, especially in situations when the field of view is narrow, since the system shake may produce highly deteriorating effects. It is important that the stabilization works for different camera types, i.e., different parts of the electromagnetic spectrum independently of the weather conditions and any form of image distortion. In this paper, we propose a method for real-time video stabilization that uses only gyroscope measurements, analyze its performance, and implement and validate it on a real-world professional electro-optical system developed at Vlatacom Institute. Camera movements are modeled with 3D rotations obtained by integration of MEMS gyroscope measurements. The 3D orientation estimation quality depends on the gyroscope characteristics; we provide a detailed discussion on the criteria for gyroscope selection in terms of the sensitivity, measurement noise, and drift stability. Furthermore, we propose a method for improving the unwanted motion estimation quality using interpolation in the quaternion domain. We also propose practical solutions for eliminating disturbances originating from gyro bias instability and noise. In order to evaluate the quality of our solution, we compared the performance of our implementation with two feature-based digital stabilization methods. The general advantage of the proposed methods is its drastically lower computational complexity; hence, it can be implemented for a low price independent of the used electro-optical sensor system

Deep Learning Based SWIR Object Detection in Long-Range Surveillance Systems: An Automated Cross-Spectral Approach

SWIR imaging bears considerable advantages over visible-light (color) and thermal images in certain challenging propagation conditions. Thus, the SWIR imaging channel is frequently used in multi-spectral imaging systems (MSIS) for long-range surveillance in combination with color and thermal imaging to improve the probability of correct operation in various day, night and climate conditions. Integration of deep-learning (DL)-based real-time object detection in MSIS enables an increase in efficient utilization for complex long-range surveillance solutions such as border or critical assets control. Unfortunately, a lack of datasets for DL-based object detection models training for the SWIR channel limits their performance. To overcome this, by using the MSIS setting we propose a new cross-spectral automatic data annotation methodology for SWIR channel training dataset creation, in which the visible-light channel provides a source for detecting object types and bounding boxes which are then transformed to the SWIR channel. A mathematical image transformation that overcomes differences between the SWIR and color channel and their image distortion effects for various magnifications are explained in detail. With the proposed cross-spectral methodology, the goal of the paper is to improve object detection in SWIR images captured in challenging outdoor scenes. Experimental tests for two object types (cars and persons) using a state-of-the-art YOLOX model demonstrate that retraining with the proposed automatic cross-spectrally created SWIR image dataset significantly improves average detection precision. We achieved excellent improvements in detection performance in various variants of the YOLOX model (nano, tiny and x)