Feature fusion is a key problem in 3D object tracking, especially in sparse and disordered point clouds scenes. The purpose of feature fusion is to achieve the communication and integration of template features and search features, so as to obtain the fusion features with object-specific information. However, most pervious Transformer-based methods use the SelfAttention Module(SAM) and Cross-Attention Module(CAM) to conduct attention operations progressively in two steps, which is not conducive to focus on the discriminative features from the beginning. Benefiting from the flexibility of attention operations, we propose a Feature-Concatenated Attention Module (FCAM) for ego-feature enhancement and cross-feature augment at the same time. Based on FCAM, we propose a Feature-Concatenated Transformer (FCT) framework to explore more effective 3D object tracking method. This scheme is more useful to achieve deeper integration and extensive communication between template and search features, which makes feature fusion more efficient. In order to verify the performance of the proposed framework, we carried out experimental verification on KITTI datasets. The results of the experiment indicate that our method is superior to the existing schemes in tracking success and accuracy for different object categories.
Aiming at the problem of image quality degradation caused by the scattering of particles in the atmosphere under foggy conditions, a polarization dehazing algorithm combining the target polarization degree and atmospheric transmission model is proposed in this paper. Firstly, a bilateral filtering method combined with image gradient information is proposed to solve four target light intensity images from different angles, and the filtered images are used to solve the polarization degree of the target. This method can preserve the edge texture information of the target in the filtering process and effectively improve the quality of the reconstructed image. Secondly, when estimating the atmospheric light intensity at infinite distance, an alpha filtering method combined with bright channels is proposed to avoid the interference of over-bright noise points in the image. The method can effectively suppress the atmospheric light intensity at infinite distance. By analyzing the experimental data, the average gradient and gray variance of the fog-free reconstructed image are significantly improved compared with the original image. Experimental results show that the proposed algorithm has strong defogging ability, and can effectively improve the image quality of the optical imaging system in foggy scenes, and realize the restoration of
To solve the problem of poor sample diversity of infrared ship image data, a method of infrared ship image data expansion is implemented based on CycleGAN. The method using the ideas of circulating, iterative approximation, there can be no noise of ship target infrared simulation image dataset maps to conform to the actual infrared detection scenario of ship target image dataset, according to the demand of the subsequent target detection identification, on the premise of the ship target itself form unchanged, injected with appropriate clutter interference, so as to realize the effective expansion of the image data, the method fully considers the target and background infrared characteristics, and are not influenced by whether the scale of the target alignment issues, which can effectively increase the image sample of diversification, for subsequent ship target detection identification algorithm, provides rich data support. The comparison experiment of image structure similarity and object detection accuracy verifies the effectiveness of the algorithm.
The polarization phenomenon of the surface of an object and its changing in different spectra include its surface spatial geometric information and material information. Based on Kirchhoff law and Jones vector , the polarization model of the emission and reflection on the surface of the object is established, and the polarization phenomenon in infrared(IR) and visible light with different materials and incident angles are simulated. The IR and visible binocular polarization imaging system was constructed and the actual polarization data of small unmanned aerial vehicle(SUAV) and buildings were obtained. Two types of characteristic parameters, the degree of polarization and the angle of polarization, were extracted and analyzed, and the results proved that the SUAV and the background of the buildings had obvious differences in IR and visible. This research provides a basis for SUAV target detection and tracking using IR and visible polarization imaging in complex backgrounds.
This paper mainly studies the berthing ship target detection method of overhead-view image under the condition of a few training samples. Because of the limited training samples, we use the complete data set unrelated to the target detection task for pre-training to obtain a classification model, then expand the data according to a certain percentage and finally complete the training of the target detection model. This paper uses the idea of segmentation to solve the target detection problem. We adjusted the configuration of the region proposal network including the size of anchor frame and the threshold of non-maximum suppression according to the target morphology, so that the network generates a more accurate region of interest. Finally, the confidence levels, bounding-boxes and image masks of multi-objective generated concurrently. We performed experiments on self-made data sets which labeled from NWPU VHR-10 and produced good results, which proved the feasibility of this method in target detection of berthing ship target.
Infrared polarization results from infrared-emitted radiation and reflected radiation effects. Polarization generated by infrared reflection is perpendicularly polarized, whereas polarization generated by infrared emission is parallelly polarized. Using the polarization feature in different directions can enhance the detection and discrimination of the target. Based on the Stokes vector, the polarization degree and angle are obtained. Then, according to the analysis of the polarization states, an orthogonality difference method of extracting polarization features is proposed. An infrared intensity and polarization feature images are fused using an algorithm of nonsubsampled shearlets transformation. Image evaluation indices of the target contrast to background (C), average gradient (AG), and image entropy (E) are employed to evaluate the fused image and original intensity image. Results demonstrate that every index of the fused image with the polarization feature is significantly improved, thereby validating the effectiveness of the proposed target-enhancement approach using polarization features extracted by the orthogonal difference method.
A method of feature extraction and small target detection, based on infrared polarization, which uses the technical superiority of infrared polarization imaging in artificial target detection to solve the clutter interference problem in infrared target detection, is proposed. First, using the differences in the polarization characteristics of the artificial target and the natural background, the infrared polarization information models for the target and background are established. The compositions of intensity information, polarization information, and target polarization information are extracted, and enhancement measures are analyzed. Then, the variable polarization theories are combined to extract the target polarization characteristics and suppress the background clutter. Finally, the infrared small target is detected, and comparisons with existing methods demonstrate the effectiveness and reliability of the proposed method.
In this paper, through analyzing the derivation process and computer simulations, we find that the update formulas for
PHD-TBD filter in paper [5] are somewhat unreasonably. When using the paper’s PHD update formula, the targets’ state
parameters cannot be estimated. Following the method in paper 2, we treat the objects to be detected in TBD situations
as special extended targets and derive a new PHD-TBD update formula analytically under this assumption. The
correctness of the derivation is validated by computer simulations.
Infrared polarization imaging is a new kind of infrared detection technology developed in recent ten years. Different
from the traditional detection method of infrared imaging, infrared polarization imaging can not only obtain infrared
radiation intensity information of targets, but also obtain the infrared radiation polarization information. So the
polarization of the target scene is the physical basis of infrared polarization imaging detection.
On the basis of the research about infrared polarization imaging theory, the characteristics of long-wave infrared
polarization detection was analyzed in this paper. Firstly, the paper studied long-wave infrared polarization state and
interaction effect which coming from the spontaneous emission of target and environment, then designed the analysis
experiment about long-wave infrared polarization characteristics that coming from spontaneous radiation, further and
verified the forming mechanism of long wave infrared polarization. Through the several experiments that the long wave
polarization information of different material objects being measured, a physical phenomenon was found that with the
long-wave thermal radiation transmitting form high temperature object to low temperature object, the polarization
characteristics transfer process had been happened at the same time, and the degree of this transfer was associated with
the material and self-temperature of the objects.
An infrared target enhancement method based on optimization in the whole directional polarization is studied in this paper. By using the description relationship between the stokes vector of incident light and the intensity of emergent light, the analytical formula between the intensity of emergent light and the polarizing angle is deduced, and thus virtually derives the intensity of emergent light from 0°to 360° polarizing angle. Then according to the criterion of maximum contrast between target and background, the searching of optimal polarizing angle is iteratively realized, and finally gets the enhanced infrared target image. The feasibility and validity of the algorithm are validated by using real long wave infrared (LWIR) polarization images of target. Experimental results show that, the enhanced image using proposed algorithm possesses obvious suppression effect of background clutter, and the quantitative evaluation under two kinds of image quality evaluation indexes of average gradient and image entropy also validates the effectiveness of our algorithm in infrared target enhancement.
After a deep study of the principle of infrared polarization imaging detection, the infrared polarization information of target and background is modeled. Considering the partial polarized light can be obtained by the superposition of natural light (unpolarized light) and linearly polarized component while ignoring the component of circularly polarized light, and combing with the degree of polarization (DOLP) and the angle of polarization (AOP), the infrared polarization information is expressed by the multiplying of an intensity factor by a polarization factor. What we have modeled not only can be used to analyze the infrared polarization information visually and profoundly, but also make the extraction of polarized features convenient. Then, faced with different application fields and based on the model, a target information enhancement program is proposed, which is achieved by extracting a linear polarization component in a certain polarized direction. This program greatly improves the contrast between target and background, and can be applied in target detection or identification, especially for camouflage or stealth target. At last, we preliminarily tested the proposed enhancement method exploiting infrared polarization images obtained indoor and outdoor, which demonstrates the effectiveness of the enhancement program.
The magnitude of light intensity on the photo-to-electric detector fluctuates all the time in an optic fiber sensing system, because of the influence of various factors in the fiber optic sensing system and from the external environment. As a result of the excessive intensity, the electric signal will be overload after the amplifier circuit with constant enlargement factor, and when the light intensity becames too small, it will reduce the signal-to-noise ratio of the electric signal. Therefore, it is necessary to introduce an automatic gain control (AGC) module into the system, which can insure the electric signal in a reasonable magnitude. In order to solve the problem of optic intensity fluctuating in the optical fiber sensing system with PGC modulation and demodulation, in this paper, firstly, it is analyzed that the impact of different magnitudes of interferential intensity to the PGC demodulation in theory. Secondly, a reasonable control method is put forward and an AGC module based on the AD602 chip is designed and produced. Finally, it is proved that the optic fiber sensor system with an AGC module has strong ability to resist fluctuation of light intensity within 40dB.
It is a difficult point to detect and recognize artificial targets under the disturbance of the complex ground clutter when remote sensing and detection to the earth. Using the different polarization information between artificial object and natural scenery, the ability to distinguish artificial targets from natural scenery can be promoted effectively. On account that the differences of polarization characteristics is an important factor in designing the target recognition method, this paper focuses attention on the application of remote sensing and reconnaissance and makes detailed research on the long wave infrared polarization characteristics of several typical metallic targets, such as aluminum plate and iron plate and the aluminum plate that be coated with black paint or yellow green camouflage. Then, the changing rules of the degree and angle of the long wave infrared polarization changing with the measurement temperature are analyzed and researched. Work of this paper lays the theoretical foundation for the design of remote sensing and detection system based on the infrared polarization information in the future.
The study of moving target detection has high research value and wide developing perspective. Considering of real-time detection of typical moving ground targets, a novel algorithm is proposed, which is based on background estimation via using Gaussian mixture model and reference background frame updating. Firstly the image gray of the target and background is supposed to obey Gaussian distribution, then the whole image is divided into three Gaussian distribution and estimated to form the reference image, finally detection results can be obtained via subtracting the reference image from current frame image. At the mean time the reference image is updated with time to keep the adaptability of the background image. Experimental results show that the algorithm is effective for moving ground targets such as vehicle.
Electronic digital image stabilization technique plays important roles in video surveillance or object acquisition.
Researchers have presented many useful algorithms, which can be classified to three kinds: gray based methods,
transformation based methods and feature based methods. When scenario is simple or flat, feature based methods
sometimes have imperfect results. Transformation based methods usually accompany large computation cost and high
computation complexity. Here we presented an algorithm based on gray projection which divided the whole image into
four sub-regions: the upper one, the bottom one, the left one and the right one. For making the translation estimation
easier, a central region is also chosen. Then the gray projections of the five sub-regions were counted. From the five pairs
of gray projections five group offsets including rotation and translation were obtained via cross correlation between
current frame and reference frame gray projections. Then according to the above offsets, the required parameters can be
estimated. The expected translation parameters(x axis offset and y axis offset) can be estimated via the offsets from the
central region image pair, the rotation angle can be calculated from the left four groups offsets. Finally, Kalman filter was
adopted to compute the compensation. Test results show that the algorithm has good estimation performance with less
than one pixel translation error and 10 percent rotation error. Based on this kind of gray projection algorithm, a real-time
electronic digital image stabilization system has been designed and implemented. System tests demonstrate the system
performance reaches the expected aim.
Focusing on the searching strategy in image matching, this paper constructs an energy function with features of a convex function based on Lyapunov Stability Theorem. It thus enables the Gradient neural network to converge steadily into the set of critical points of the target function. Then this paper tries to apply the network in image matching with moment invariants as the feature parameter. The specific steps of the experiments are supplied in this paper. According to the results of the experiments, this matching algorithm features good convergence, high speed, wide applicability and an extraordinary matching effect.
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