The research of visual tracking is of great significance for intelligent video surveillance, unmanned vehicle, manmachine interaction, intelligent robot, unmanned aerial vehicle reconnaissance and other fields. It has been a hot spot in the field of computer vision and pattern recognition. It provides a fundamental component for high-level video understanding applications such as motion analysis, event detection and activity recognition. The target tracking system mounted on uav platform is of high application value. However, in the actual development process, it is difficult to develop due to the fact that the Hisilicon platform has a series of special hardware modules and only supports the deep model under the framework of Caffe. We deployed some discriminative correlation filter algorithms and deep learning tracker on Hi3559A, and made a comparison of their performance and speed.
Oil tank is one kind of foundational industrial facility for storage of oil and petrochemical products. Automatic recognition of the oil depot in the remote sensing image is of important practical significance in many fields. Nowadays, the Unmanned Aerial Vehicle (UAV) provides an available alternative solution to the satellite for monitoring the oil depot, owing to its advantages of flexibility, rapid response and minimal cost. In this paper, a novel oil tank extraction method based on detection of the elliptic rooftop is proposed. To start with, straight line segments of object boundary are extracted in the UAV imagery. Secondly, these lines are linked to form arc segments based on proper geometric criteria, and then elliptical rooftops are extracted based on these arcs to generate hypotheses of potential oil tanks. Finally, within Region of Interest (ROI) of rooftops, hypotheses disambiguation and verification of targets are accomplished primarily by extraction of facade contours of oil tanks. Experimental results demonstrate the good performance of our method on a variety of complex scenes.
With regard to aircraft target detection in complex clouds background of infrared search system, this paper proposes a new target detection algorithm based on combination of intensity and edge of the target. Firstly, the algorithm segments the image by iterative OTSU segmentation method, at the same time, it detects the edge by morphological processing. Then, by the fusion decision of the combination of segmentation and edge result, it detects the real aircraft targets and eliminates the clouds false alarm. The algorithm overcomes the too much clouds false alarm problems of the traditional target detection method. The test data detection shows, the algorithm enables effective detection of aircraft target in complex clouds background with low-rate false warning. The algorithm has realized real-time processing and has been effectively applied to the development of the engineering sample of the Wide Field of View Infrared Search System.
Laser detection based on the “cat’s eye effect” has become the hot research project for its initiative compared to the passivity of sound detection and infrared detection. And the target detection is one of the core technologies in this system. The paper puts forward a method for detecting small targets based on cumulative weighted value of target properties using given data. Firstly, we make a frame difference to the images, then make image processing based on Morphology Principles. Secondly, we segment images, and screen the targets; then find some interesting locations. Finally, comparing to a quantity of frames, we locate the target. We did an exam to 394 true frames, the experimental result shows that the mathod can detect small targets efficiently.
High-resolution, multi-pixels and large field of view (FOV) infrared (IR) detector is an important research direction, which greatly improves the target detection capability. This paper addresses the infrared target detection under the
guidance of attention mechanism. The Gabor filter is used to extract the elementary visual feature of infrared image for its
orientation selectiveness. Then it researches the reasons that produce visual saliency in frequency domain, and provides the
multichannel feature combination strategy to generate the feature map. Further, a novel saliency detection model using Fourier spectrum filtering, is presented to calculate feature regions of infrared image. Experimental results using a wide range of real IR images demonstrate that the proposed algorithm is robust and effective, yielding satisfying results for infrared target detection in large FOV with complex background and low SNR.
In order to implement real-time detection of hedgehopping target in large view-field infrared (LVIR) image, the
paper proposes a fast algorithm flow to extract the target region of interest (ROI). The ground building region was
rejected quickly and target ROI was segmented roughly through the background classification. Then the background
image containing target ROI was matched with previous frame based on a mean removal normalized product correlation
(MRNPC) similarity measure function. Finally, the target motion area was extracted by inter-frame difference in time
domain. According to the proposed algorithm flow, this paper designs the high-speed real-time signal processing
hardware platform based on FPGA + DSP, and also presents a new parallel processing strategy that called function-level
and task-level, which could parallel process LVIR image by multi-core and multi-task. Experimental results show that
the algorithm can extract low altitude aero target with complex background in large view effectively, and the new design
hardware platform could implement real time processing of the IR image with 50000x288 pixels per second in large
view-field infrared search system (LVIRSS).
With regard to target detection in complex background in high resolution image sequences attained by Wide Field of View Infrared Surveillance System, a rough-to-meticulous real-time target detection algorithm is proposed. In the rough detection phase, it attains initial high rate target detection by background matching and frame difference algorithm, based on the gray high frequency and moving characteristics of the target in the wide field of view image. In the meticulous recognition phase, focusing on the detected suspected target sliced images, it has further delicate recognition on the basis of targets’ characteristics to exclude those false jamming. The detection result of the test images shows, the algorithm enables stable detection with low-rate false alarm for distant dim targets, and has been applied to the signal processing of the Wide Field of View Infrared Surveillance System.
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