• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.10 no.11
• /
• pp.5624-5638
• /
• 2016

A novel method based on Surf points is proposed to detect and lock-track single ground target in aerial videos. Videos captured by moving cameras contain complex motions, which bring difficulty in moving object detection. Our approach contains three parts: moving target template detection, search area estimation and target tracking. Global motion estimation and compensation are first made by grids-sampling Surf points selecting and matching. And then, the single ground target is detected by joint spatial-temporal information processing. The temporal process is made by calculating difference between compensated reference and current image and the spatial process is implementing morphological operations and adaptive binarization. The second part improves KALMAN filter with surf points scale information to predict target position and search area adaptively. Lastly, the local Surf points of target template are matched in this search region to realize target tracking. The long-term tracking is updated following target scaling, occlusion and large deformation. Experimental results show that the algorithm can correctly detect small moving target in dynamic scenes with complex motions. It is robust to vehicle dithering and target scale changing, rotation, especially partial occlusion or temporal complete occlusion. Comparing with traditional algorithms, our method enables real time operation, processing $520{\times}390$ frames at around 15fps.

• Journal of the Optical Society of Korea
• /
• v.20 no.6
• /
• pp.745-751
• /
• 2016

A detection algorithm, based on the combined local-global (CLG) optical-flow model and Gaussian pyramid for a moving target appearing against a dynamic background, can compensate for the inadaptability of the classic Horn-Schunck algorithm to illumination changes and reduce the number of needed calculations. Incorporating the hypothesis of gradient conservation into the traditional CLG optical-flow model and combining structure and texture decomposition enable this algorithm to minimize the impact of illumination changes on optical-flow estimates. Further, calculating optical-flow with the Gaussian pyramid by layers and computing optical-flow at other points using an optical-flow iterative with higher gray-level points together reduce the number of calculations required to improve detection efficiency. Finally, this proposed method achieves the detection of a moving target against a dynamic background, according to the background motion vector determined by the displacement and magnitude of the optical-flow. Simulation results indicate that this algorithm, in comparison to the traditional Horn-Schunck optical-flow algorithm, accurately detects a moving target undergoing illumination changes against a dynamic background and simultaneously demonstrates a significant reduction in the number of computations needed to improve detection efficiency.

• Journal of the Institute of Electronics Engineers of Korea SC
• /
• v.47 no.6
• /
• pp.27-32
• /
• 2010

77GHz FMCW(Frequency Modulation Continuous Wave) radar system has been used for automotive active safety systems. In typical automotive radar, the moving target detection and clutter cancellation including stationary targets are very important signal processing algorithms. This paper proposed the moving target detection algorithm which improve the detection probability and reduce the false alarm rate. First, the proposed moving target beat-frequency extraction filter is used in order to suppress clutter, and then the data association is applied by using the extracted moving target beat-frequency. Then, the zero-Doppler target is eliminated to remove the rest of clutter.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
• /
• v.28 no.8
• /
• pp.661-669
• /
• 2017

This paper describes an MMTI(Maritime Moving Target Indicator) for periscope detection in airborne radar. Firstly, we analyze the characteristics of sea clutter, sea targets. Secondly, we study the differences between GMTI(Ground Moving Target Indicator) and MMTI. This paper proposes an optimal MMTI operating environment and method. We also suggest a signal processing algorithm using STAP(Space-Time Adaptive Processing) for detecting small RCS target moving low speed. The detection probability for moving target with MDV(Minimum Detectable Velocity) is simulated under various RCS and multi-channel system. Finally, we analyze the major performance for range, velocity and azimuth accuracy.

• 제어로봇시스템학회:학술대회논문집
• /
• 1994.10a
• /
• pp.494-497
• /
• 1994

We propose a novel velocity detection method of moving object based on a speckle pattern on the target surface using a self-mixing laser diode (SMLD). By this measurement, it was confirmed that the speckle signal has its waveform independent of the target velocity, and has its averaged frequency directly proportional to the target velocity. So it will be possible to detect the velocity of the target transversely translating against the laser light beam using a compact measuring system.

• Journal of the Korean Institute of Telematics and Electronics D
• /
• v.34D no.7
• /
• pp.88-96
• /
• 1997

As the need of automatized system has been increased recently together with the development of industrial and military technologies, the adaptive real-time target detection technologies that can be embedded on vehicles, planes, ships, robots and so on, are hgihly demanded. Accordingly, this paper proposes a novel approach to detect and segment the moving targets using the binary phase extraction joint transform correlator (BPEJTC), the advanced image subtraction filter and convex hull processing. The BPEJTC which was used as a target detection unit mainly for target tracking compensating the camera movement. The target region has been detected by processing the successful three frames using the advanced image subtraction filter, and has become more accurate by applying the developed convex hull filter. As shown by some experimental results, it is expected that the proposed approaches for compensation of the camera movement and segmentationof of target region, can be used for th emissile guiddance, aero surveillance, automatic inspectin system as well as the target detection unit of automatic target recognition system that request adaptive real-time processing.

• ETRI Journal
• /
• v.36 no.5
• /
• pp.799-807
• /
• 2014

In this paper, we design a rake-based cellular radar receiver (CRR) scheme to detect a moving target located in a multipath environment. The modules of Doppler filter banks, threshold level test, and target detection module are newly introduced into the conventional rake receiver so that it can function as a radar system. The proposed CRR tests the Doppler-shift frequency and signal-to-noise ratio of the received signal against predefined threshold levels to determine detection and then calculates target velocities and ranges. The system performance is evaluated in terms of detection probability and the maximum detection range under a Nakagami-n channel that reflects the multipath environment.

• Journal of Institute of Control, Robotics and Systems
• /
• v.16 no.11
• /
• pp.1044-1052
• /
• 2010

In this paper, a new realtime algorithm called the RTPBTD-HPDAF (Recursive Temporal Profile Base Target Detection with Highest Probability Data Association Filter) is presented for tracking fast moving small targets with IIR (Imaging Infrared) sensor systems. Spatial filter algorithms are mainly used for target in IIR sensor system detection and tracking however they often generate high density clutter due to various shapes of cloud. The TPBTD (Temporal Profile Base Target Detection) algorithm based on the analysis of temporal behavior of individual pixels is known to have good performance for detection and tracking of fast moving target with suppressing clutter. However it is not suitable to detect stationary and abruptly maneuvering targets. Moreover its computational load may not be negligible. The PTPBTD-HPDAF algorithm proposed in this paper for real-time target detection and tracking is shown to be computationally cheap while it has benefit of tracking targets with abrupt maneuvers. The performance of the proposed RTPBTD-HPDAF algorithm is tested and compared with the spatial filter with HPDAF algorithm for run-time and track initiation at real IIR video.

• 제어로봇시스템학회:학술대회논문집
• /
• 2003.10a
• /
• pp.673-676
• /
• 2003

This paper deals with the problem of moving object detection and location in computer vision. We describe a new object-dependent motion analysis method for tracking target in an image sequence taken from a moving platform. We tackle these tasks with three steps. First, we make an active contour model of a target in order to build some of low-energy points, which are called kernels. Then we detect interest points in two windows called tracking windows around a kernel respectively. At the third step, we decide the correspondence of those detected interest points between tracking windows by the probabilistic relaxation method In this algorithm, the detecting process is iterative and begins with the detection of all potential correspondence pair in consecutive image. Each pair of corresponding points is then iteratively recomputed to get a globally optimum set of pairwise correspondences.

• Journal of the Korea Society of Computer and Information
• /
• v.17 no.10
• /
• pp.43-53
• /
• 2012

In the this paper, a gesture-based user interface based on object detection using predefined gesture and the tracking of the detected object is proposed. For object detection, moving objects in a frame are computed by comparing multiple previous frames and predefined gesture is used to detect the target object among those moving objects. Any object with the predefined gesture can be used to control. We also propose an object tracking algorithm, namely density based meanshift algorithm, that uses color distribution of the target objects. The proposed object tracking algorithm tracks a target object crossing the background with a similar color more accurately than existing techniques. Experimental results show that the proposed object detection and tracking algorithms achieve higher detection capability with less computational complexity.