• Proceedings of the Korean Society of Broadcast Engineers Conference
• /
• 2009.01a
• /
• pp.174-177
• /
• 2009

In object tracking, the template matching methods have been developed and frequently used. It is fast enough, but not robust to an object with the variation of size and shape. In order to overcome the limitation of the template matching method, this paper proposes a template update technique. After finding an object position using the correlation-based adaptive predictive search, the proposed method selects blocks which contain object's boundary. It estimates the motion of boundary using block matching, and then updates template. We applied it to IR image sequences including an approaching object. From the experimental results, the proposed method showed successful performance to track object.

• Proceedings of the Korean Society of Broadcast Engineers Conference
• /
• 2012.07a
• /
• pp.269-270
• /
• 2012

Features which exhibit scale and rotation invariance, such as SIFT, are notorious for expensive computation time, and often overlooked for real-time tracking scenarios. This paper proposes a descriptorless matching algorithm based on motion vectors between consecutive frames to find the geometrically closest candidate to each tracked reference feature in the database. Descriptor-less matching forgoes expensive SIFT descriptor extraction without loss of matching accuracy and exhibits dramatic speed-up compared to traditional, naive matching based trackers. Descriptor-less SIFT tracking runs in real-time on an Intel dual core machine at an average of 24 frames per second.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.15 no.8
• /
• pp.3011-3024
• /
• 2021

Pose estimation of the sensor is important issue in many applications such as robotics, navigation, tracking, and Augmented Reality. This paper proposes visual-inertial integration system appropriate for dynamically moving condition of the sensor. The orientation estimated from Inertial Measurement Unit (IMU) sensor is used to calculate the essential matrix based on the intrinsic parameters of the camera. Using the epipolar geometry, the outliers of the feature point matching are eliminated in the image sequences. The pose of the sensor can be obtained from the feature point matching. The use of IMU sensor can help initially eliminate erroneous point matches in the image of dynamic scene. After the outliers are removed from the feature points, these selected feature points matching relations are used to calculate the precise fundamental matrix. Finally, with the feature point matching relation, the pose of the sensor is estimated. The proposed procedure was implemented and tested, comparing with the existing methods. Experimental results have shown the effectiveness of the technique proposed in this paper.

• Proceedings of the KSRS Conference
• /
• 2003.11a
• /
• pp.882-884
• /
• 2003

We present an application of digital video images for object tracking. In order to track a fixed object, which was shoot on a moving vehicle, this study develops a shape-based matching algorithm to implement the tracking task. Because the shape-based matching algorithm has scale and rotation invariant characteristics, therefore it can be used to calculate the similarity between two variant shapes. An experiment is performed to track the ship object in the open sea. The result shows that the proposed method can track the object in the video images even the shape change largely.

• Proceedings of the IEEK Conference
• /
• 2006.06a
• /
• pp.295-296
• /
• 2006

This paper implements a method that tracking the moving objects that detected by the motion detection function of the digital CCTV system. We simply implement the motion detection function of the digital CCTV system that use frame difference and thresholding. When motion is detected, the motion detection function generates two outputs. One output is the event that the motion is arised in input image frame. The other output is coordinate that motion is exists. Then, do the block matching algorithm[2] using coordinate, that motion is exists, as initial coordinate of the block matching algorithm. The best matched coordinate is new initial coordinate of the block matching algorithm for the next image frame. We simply use the block matching algorithm that implements tracking the moving objects. It is simple, but useful the actual digital CCTV system.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.17 no.6
• /
• pp.1635-1656
• /
• 2023

In Bayesian multi-target tracking, the Poisson multi-Bernoulli mixture (PMBM) filter is a state-of-the-art filter based on the methodology of random finite set which is a conjugate prior composed of Poisson point process (PPP) and multi-Bernoulli mixture (MBM). In order to improve the random finite set-based filter utilized in multi-target tracking of sensor scanning, this paper introduces the Poisson multi-Bernoulli mixture filter into time-matching Bayesian filtering framework and derive a tractable and principled method, namely: the time-matching Poisson multi-Bernoulli mixture (TM-PMBM) filter. We also provide the Gaussian mixture implementation of the TM-PMBM filter for linear-Gaussian dynamic and measurement models. Subsequently, we compare the performance of the TM-PMBM filter with other RFS filters based on time-matching method with different birth models under directional continuous scanning and out-of-order discontinuous scanning. The results of simulation demonstrate that the proposed filter not only can effectively reduce the influence of sampling time diversity, but also improve the estimated accuracy of target state along with cardinality.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.17 no.4
• /
• pp.1066-1079
• /
• 2023

Multi-target tracking based on the detector is a very hot and important research topic in target tracking. It mainly includes two closely related processes, namely target detection and target tracking. Where target detection is responsible for detecting the exact position of the target, while target tracking monitors the temporal and spatial changes of the target. With the improvement of the detector, the tracking performance has reached a new level. The problem that always exists in the research of target tracking is the problem that occurs again after the target is occluded during tracking. Based on this question, this paper proposes a DeepSORT model based on SIFT features to improve ship tracking. Unlike previous feature extraction networks, SIFT algorithm does not require the characteristics of pre-training learning objectives and can be used in ship tracking quickly. At the same time, we improve and test the matching method of our model to find a balance between tracking accuracy and tracking speed. Experiments show that the model can get more ideal results.

• Proceedings of the IEEK Conference
• /
• 2002.07c
• /
• pp.1491-1494
• /
• 2002

The paper introduces a novel system of two hands real-time tracking based on the unrestricted hand skin segmentation by multi color systems. After corer-based segmentation and pre-processing operation, a label set of regions is created to locate the two hands automatically. By the normalization, template matching is used to find out the left or right hand. An improved fast self-adaptive tracking algorithm is applied and Canny filter is used for hand detection.

• 제어로봇시스템학회:학술대회논문집
• /
• 1987.10b
• /
• pp.109-114
• /
• 1987

In this thesis, we work on the curve recognition with real time processing and the Robot tracking method on recognized curve. Image information of segment curve is supplied to computer to run to a Robot so that it is a feedback system. Image coordinate frame to world coordinate transformation represents in this paper and curve matching algorithm subscribes by two method, first transformation matching algorithm, second image coordinate matching algorithm. Also Robot running time to computer image processing time relationships finally includes.

• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.15 no.2
• /
• pp.455-461
• /
• 2011

In object tracking using the block mating algorithm, it is not proper to use a fixed matching block to track an object of which size may be various and can be changed at any time. This paper defines an adaptive matching block for the dynamic environment and proposes a block matching algorithm for it. The matching block is composed of a main-block of $10{\times}10$ pixels and 8 sub-blocks of $6{\times}6$ pixels in a wide area of $42{\times}42$ pixels, the main-block located its center is used as an object block, and the sub-blocks located its boundary are used as candidates for the object block. The proposed algorithm extracts the object blocks from the sub-blocks by using their motion vectors for 10 previous frames and performs the block matching with the main block and them. The experiments for perform estimation show that the proposed algorithm extracts just valid object blocks from the matching block and keeps an object having free movement in image center area.