• Journal of the Institute of Electronics Engineers of Korea CI
• /
• v.41 no.4
• /
• pp.85-94
• /
• 2004

Object tracking in a real time image is one of interesting subjects in computer vision and many Practical application fields during the past couple of years. But sometimes existing systems cannot find all objects by recognizing background noise as object. This paper proposes a method of object detection and tracking using adaptive background image in real time. To detect object which is not influenced by illumination and to remove noise in background image, this system generates adaptive background image by real time background image updating. This system detects object using the difference between background image and input image from camera. After setting up Minimum Bounding Rectangle(MBR) using the internal point of detected object, the system tracks object through this MBR In addition, this paper evaluates the test result about performance of proposed method as compared with existing tracking algorithm.

• Transactions of the Korean Society of Automotive Engineers
• /
• v.24 no.2
• /
• pp.127-136
• /
• 2016

The camera based object detection systems should satisfy the recognition performance as well as real-time constraints. Particularly, in safety-critical systems such as Autonomous Emergency Braking (AEB), the real-time constraints significantly affects the system performance. Recently, multi-core processors and system-on-chip technologies are widely used to accelerate the object detection algorithm by distributing computational loads. However, due to the advanced hardware, the complexity of system architecture is increased even though additional hardwares improve the real-time performance. The increased complexity also cause difficulty in migration of existing algorithms and development of new algorithms. In this paper, to improve real-time performance and design complexity, a task scheduling strategy is proposed for visual object tracking systems. The real-time performance of the vision algorithm is increased by applying pipelining to task scheduling in a multi-core processor. Finally, the proposed task scheduling algorithm is applied to crosswalk detection and tracking system to prove the effectiveness of the proposed strategy.

• International Journal of Advanced Culture Technology
• /
• v.4 no.4
• /
• pp.81-86
• /
• 2016

In this paper, we propose a simple and effective vision-based tracking controller design for autonomous object tracking using multicopter. The multicopter based automatic tracking system usually unstable when the object moved so the tracking process can't define the object position location exactly that means when the object moves, the system can't track object suddenly along to the direction of objects movement. The system will always looking for the object from the first point or its home position. In this paper, PID control used to improve the stability of tracking system, so that the result object tracking became more stable than before, it can be seen from error of tracking. A computer vision and control strategy is applied to detect a diverse set of moving objects on Raspberry Pi based platform and Software defined PID controller design to control Yaw, Throttle, Pitch of the multicopter in real time. Finally based series of experiment results and concluded that the PID control make the tracking system become more stable in real time.

• IEMEK Journal of Embedded Systems and Applications
• /
• v.10 no.6
• /
• pp.363-372
• /
• 2015

Recent some SoC FPGA Releases that integrate ARM processor and FPGA fabric show better performance compared to the ASIC SoC used in typical embedded image processing system. In this study, using the above advantages, we implement a SoC FPGA-based Real-Time Object Recognition and Tracking System. In our system, the video input and output, image preprocessing process, and background subtraction processing were implemented in FPGA logics. And the object recognition and tracking processes were implemented in ARM processor-based programs. Our system provides the processing performance of 5.3 fps for the SVGA video input. This is about 79 times faster processing power than software approach based on the Nios II Soft-core processor, and about 4 times faster than approach based the HPS processor. Consequently, if the object recognition and tracking system takes a design structure combined with the FPGA logic and HPS processor-based processes of recent SoC FPGA Releases, then the real-time processing is possible because the processing speed is improved than the system that be handled only by the software approach.

• The Journal of Information Technology
• /
• v.10 no.4
• /
• pp.27-39
• /
• 2007

Object tracking in a real time environment is one of challenging subjects in computer vision area during past couple of years. This paper proposes a method of object detection and tracking using adaptive background estimation in real time environment. To obtain a stable and adaptive background, we combine 3-frame differential method and running average single gaussian background model. Using this background model, we can successfully detect moving objects while minimizing false moving objects caused by noise. In the tracking phase, we propose a matching criteria where the weight of position and inner brightness distribution can be controlled by the size of objects. Also, we adopt a Kalman Filter to overcome the occlusion of tracked objects. By experiments, we can successfully detect and track objects in real time environment.

• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.10 no.1
• /
• pp.191-196
• /
• 2006

This paper presents the moving object tracking method using vision system. In order to track object in real time, the image of moving object have to be located the origin of the image coordinate axes. Accordingly, Fuzzy Control System is investigated for tracking the moving object, which control the camera module with Pan/Tilt mechanism. Hereafter, so the this system is applied to mobile robot, we design and implement image processing board for vision system. Also fuzzy controller is implemented to the StrongArm board. Finally, the proposed fuzzy controller is useful for the real-time moving object tracking system by experiment.

• Journal of Korea Multimedia Society
• /
• v.22 no.2
• /
• pp.167-177
• /
• 2019

Even though so much progresses have been achieved in Multiple Object Tracking (MOT), most of reported MOT methods are not still satisfactory for commercial embedded products like Pan-Tilt-Zoom (PTZ) camera. In this paper, we propose a real-time multiple pedestrians tracking method for embedded environments. First, we design a new light weight convolutional neural network(CNN)-based pedestrian detector, which is constructed to detect even small size pedestrians, as well. For further saving of processing time, the designed detector is applied for every other frame, and Kalman filter is employed to predict pedestrians' positions in frames where the designed CNN-based detector is not applied. The pose orientation information is incorporated to enhance object association for tracking pedestrians without further computational cost. Through experiments on Nvidia's embedded computing board, Jetson TX2, it is verified that the designed pedestrian detector detects even small size pedestrians fast and well, compared to many state-of-the-art detectors, and that the proposed tracking method can track pedestrians in real-time and show accuracy performance comparably to performances of many state-of-the-art tracking methods, which do not target for operation in embedded systems.

• ETRI Journal
• /
• v.37 no.3
• /
• pp.551-561
• /
• 2015

We propose a novel multiple-object tracking algorithm for real-time intelligent video surveillance. We adopt particle filtering as our tracking framework. Background modeling and subtraction are used to generate a region of interest. A two-step pedestrian detection is employed to reduce the computation time of the algorithm, and an iterative particle repropagation method is proposed to enhance its tracking accuracy. A matching score for greedy data association is proposed to assign the detection results of the two-step pedestrian detector to trackers. Various experimental results demonstrate that the proposed algorithm tracks multiple objects accurately and precisely in real time.

• Journal of Korea Society of Industrial Information Systems
• /
• v.17 no.4
• /
• pp.51-59
• /
• 2012

Recently, the study about object tracking using image processing has been active in the field of security and surveillance. Existing security and surveillance systems using multiple cameras have been operating independently. Thus, the chase was difficult when the tracking object move to other monitored areas. In this paper, we propose the way to change the control of camera automatically following the moving direction of objects in multiple cameras. The proposed method detects the object and tracks the object using color information and direction information of object. The color information obtains using the hue and the direction information obtains using the optical flow. At this time, the optical flow is detected for the entire image area of an object that is not applied only to reduce the computational complexity makes it possible to track in real time. In addition, it can be solved to inconvenience of security surveillance system to use existing camera by tracking an object automatically.

• Journal of the Korea Society of Computer and Information
• /
• v.7 no.4
• /
• pp.52-58
• /
• 2002

This Paper describes the method of real time object tracking using variable searching window. Monitoring systems require real time object tracking in video, efficiencies depend on environment of monitoring target. To get a position of object using a difference between background image and input image, the system extracts contour and centroid of the object. This method track motion of object using variable searching window from size and position of object. The background imgaes and camera are limited as fixed environment. The test result of proposed method Is 17-23FPS, this shows more fast process speed than average(10-14FPS) of existing object tracking method.