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

• Journal of Digital Convergence
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• v.13 no.12
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• pp.209-215
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• 2015

In the past, there have been many theory and algorithms for vehicle tracking. But the time complexity of many feature point matching methods for vehicle tracking are exponential. Also, object segmentation and detection algorithms presented for vehicle tracking are exhaustive and time consuming. Therefore, we present the fast and efficient two stages method that can efficiently track the many moving vehicles on the road. The first detects the vehicle plate regions and extracts the feature points of vehicle plates. The second associates the feature points between frames using dynamic programming.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.40 no.1
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• pp.35-42
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• 2012

In this study, a feature-points-tracking algorithm is suggested using a particle filter for vision based navigation system. By applying a dynamic model of the feature point, the tracking performance is increased in high dynamic condition, whereas a conventional KLT (Kanade-Lucas-Tomasi) cannot give a solution. Futhermore, the particle filter is introduced to cope with irregular characteristics of vision data. Post-processing of recorded vision data shows that the tracking performance of suggested algorithm is more robust than that of KLT in high dynamic condition.

• The Journal of the Acoustical Society of Korea
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• v.33 no.4
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• pp.225-231
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• 2014

This paper suggests an algorithm to improve the tracking performance for an underwater acoustic target using the feedback information of acoustic feature of a target. While conventional tracking algorithms use detected acoustic signals only, the proposed algorithm uses detected acoustic signals and target feature information as well. Since the proposed algorithm tracks only the selected measurements using target feature information, it prevents onset of unnecessary tracks and improves tracking performance for target of interest. Furthermore, it optimizes tracking parameters for the target of interest and guarantees robustness and consistency of the track. Some simulations are performed to demonstrate the improved tracking performance of the proposed algorithm.

• Proceedings of the IEEK Conference
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• 2009.05a
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• pp.408-410
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• 2009

We present an efficient and robust measurement model for visual tracking. This approach builds on and extends work on subspace representations of measurement model. Subspace-based tracking algorithms have been introduced to visual tracking literature for a decade and show considerable tracking performance due to its robustness in matching. However the measures used in their measurement models are often restricted to few approaches. We propose a novel measure of object matching using Angle In Feature Space, which aims to improve the discriminability of matching in subspace. Therefore, our tracking algorithm can distinguish target from similar background clutters which often cause erroneous drift by conventional Distance From Feature Space measure. Experiments demonstrate the effectiveness of the proposed tracking algorithm under severe cluttered background.

• Journal of the Korean Institute of Intelligent Systems
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• v.19 no.4
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• pp.586-591
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• 2009

In this paper, we propose the mobile object tracking algorithm based on the feature vector using particle filter. To do this, first, we detect the movement area of mobile object by using RGB color model and extract the feature vectors of the input image by using the KLT-algorithm. And then, we get the first feature vectors by matching extracted feature vectors to the detected movement area. Second, we detect new movement area of the mobile objects by using RGB and HSI color model, and get the new feature vectors by applying the new feature vectors to the snake algorithm. And then, we find the second feature vectors by applying the second feature vectors to new movement area. So, we design the mobile object tracking algorithm by applying the second feature vectors to particle filter. Finally, we validate the applicability of the proposed method through the experience in a complex environment.

• Journal of Korea Multimedia Society
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• v.23 no.10
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• pp.1236-1249
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• 2020

In a most recent object tracking research work, applying Convolutional Neural Network and Recurrent Neural Network-based strategies become relevant for resolving the noticeable challenges in it, like, occlusion, motion, object, and camera viewpoint variations, changing several targets, lighting variations. In this paper, the LSTM Network-based Tracking association method has proposed where the technique capable of real-time multi-object tracking by creating one of the useful LSTM networks that associated with tracking, which supports the long term tracking along with solving challenges. The LSTM network is a different neural network defined in Keras as a sequence of layers, where the Sequential classes would be a container for these layers. This purposing network structure builds with the integration of tracking association on Keras neural-network library. The tracking process has been associated with the LSTM Network feature learning output and obtained outstanding real-time detection and tracking performance. In this work, the main focus was learning trackable objects locations, appearance, and motion details, then predicting the feature location of objects on boxes according to their initial position. The performance of the joint object tracking system has shown that the LSTM network is more powerful and capable of working on a real-time multi-object tracking process.

• The KIPS Transactions:PartB
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• v.18B no.4
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• pp.201-212
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• 2011

In this paper, we suggest a system of detecting a vehicle with lane violation, which can detect the vehicle with lane violation, by using the feature point tracking. The whole algorism in the suggested system of detecting a vehicle with lane violation is composed of three stages such as feature extraction, register and tracking in feature for the tracking-targeted vehicle, and detecting a vehicle with lane violation. The feature is extracted from the morphological gradient image, which results in constructing robust detection system against shadows, weather conditions, head lights and illumination conditions without distinction day and night. The system shows excellent performance for the data captured at day time, night time, and rainy night time as much as 99.49% for positive recognition ratio and 0.51% for error ratio. Also the system is so fast as much as 91.34 frames per second in average that it may be possible for real-time processing.

• 제어로봇시스템학회:학술대회논문집
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• 1996.10a
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• pp.145-148
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• 1996

In moving object tracking based on the visual sensory feedback, a prerequisite is to determine which feature or which object is to be tracked and then the feature or the object identification precedes the tracking. In this paper, we focus on the object identification not image feature identification. The target identification is realized by finding out corresponding line segments to the hypothesized model segments of the target. The key idea is the combination of the Mahalanobis distance with the geometrica relationship between model segments and extracted line segments. We demonstrate the robustness and feasibility of the proposed target identification algorithm by a moving vehicle identification and tracking in the video traffic surveillance system over images of a road scene.

• IEMEK Journal of Embedded Systems and Applications
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• v.11 no.4
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• pp.219-225
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• 2016

Visual object tracking is one of the key tasks in computer vision. Robust trackers should address challenging issues such as fast motion, deformation, occlusion and so on. In this paper, we therefore propose a visual object tracking method that exploits inter-frame correlations of convolutional feature maps in Convolutional Neural Net (ConvNet). The proposed method predicts the location of a target by considering inter-frame spatial correlation between target location proposals in the present frame and its location in the previous frame. The experimental results show that the proposed algorithm outperforms the state-of-the-art work especially in hard-to-track sequences.