Visual tracking of a moving target using active contour based SSD algorithm
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Corresponding author. Fax: +82 2 821 7653. E-mail addresses: young@ssu.ac.kr (Y. Han), hahn@ssu.ac.kr (H. Hahn).
092ቤተ መጻሕፍቲ ባይዱ-8890/$ – see front matter © 2005 Elsevier B.V. All rights reserved. doi:10.1016/j.robot.2005.09.005
School of Electronic Engineering, Soongsil University, 1-1, Sangdo5Dong, Dongjak-Ku, Seoul 156-743, Republic of Korea Received 13 April 2004; received in revised form 18 June 2005; accepted 7 September 2005 Available online 28 October 2005
Robotics and Autonomous Systems 53 (2005) 265–281
Visual tracking of a moving target using active contour based SSD algorithm
Youngjoon Han, Hernsoo Hahn ∗
Abstract This paper presents a new image based visual tracking scheme for a mobile robot to trace a moving target using a single camera mounted on the mobile robot. To accurately estimate the position of the target in the next image, it decomposes the effect of the camera motion on the velocity vector of the target in the image frame. Based on the estimated velocity of the target and the image Jacobian, the control inputs of the mobile robot are determined in such a way that the target may appear inside the central area of the image frame. Since the shape of the target in the image frame varies due to rotation and translation of the target, a new shape adaptive Sum-of-Squared Difference (SSD) algorithm is proposed which uses the extended snake algorithm to extract the contour of the target and updates the template in every step of the matching process. The proposed scheme has been implemented using a Nomad Scout Robot II. The experimental results have shown that the proposed scheme follows the target within a negligible error range even when the target is temporarily lost due to various reasons. © 2005 Elsevier B.V. All rights reserved.
Keywords: Image based visual tracking; Mobile robot; Shape adaptive SSD algorithm; Extended snake algorithm
1. Introduction The function of tracking a target has become an essential one for surveillance systems and mobile robots. This function is used for automatically monitoring the motion of a suspicious person or an intruder in surveillance systems [1], and for following a target with various purposes in mobile robots [2]. Although many types of sensors have been used for implementing this function with their own advantages, vision system is most widely used for almost all applications because it can provide the information on the target with variant field of view or with different resolutions [3,4].
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Y. Han, H. Hahn / Robotics and Autonomous Systems 53 (2005) 265–281
Tracking a target using a vision system can be classified into two cases: one is the case where the input device of the vision system (camera) has a fixed pose (position and orientation) and another one is the case where the camera can change its pose. When the pose of a camera is fixed, tracking a target is to locate the target in the sequence of input images [5,6]. In this case, the task of tracking a target becomes a problem of detecting and locating a target in the input image. If the pose of the camera can be changed, the problem becomes much more complex. In this case, tracking a target is not only to detect and locate the target in the input image but also to control the pose of the camera to keep the target inside the input image [7,8]. If the camera is located on a mobile robot (or a manipulator), its pose can be changed by controlling the pose of the mobile robot (or a manipulator). Controlling the pose of mobile robot using a vision system to track a target is called visual tracking. The tracking has either been performed in the image (image based visual servoing) or in the world coordinates (position based visual servoing). The position based one controls the robot using its positional information of the target represented in the 3D Cartesian space [9–11]. Since it uses the feature points of the target perspectively projected in the image frame of the camera to determine the relative pose of the target, it requires the information on the feature points of the target to be known a priori. To reduce the tracking error of a moving object, Saedan et al. [12] proposed the closed target pose estimation method. The control system consists of the visual control and the robot servo control. The visual control tolerates the errors due to camera calibration. On the
Corresponding author. Fax: +82 2 821 7653. E-mail addresses: young@ssu.ac.kr (Y. Han), hahn@ssu.ac.kr (H. Hahn).
092ቤተ መጻሕፍቲ ባይዱ-8890/$ – see front matter © 2005 Elsevier B.V. All rights reserved. doi:10.1016/j.robot.2005.09.005
School of Electronic Engineering, Soongsil University, 1-1, Sangdo5Dong, Dongjak-Ku, Seoul 156-743, Republic of Korea Received 13 April 2004; received in revised form 18 June 2005; accepted 7 September 2005 Available online 28 October 2005
Robotics and Autonomous Systems 53 (2005) 265–281
Visual tracking of a moving target using active contour based SSD algorithm
Youngjoon Han, Hernsoo Hahn ∗
Abstract This paper presents a new image based visual tracking scheme for a mobile robot to trace a moving target using a single camera mounted on the mobile robot. To accurately estimate the position of the target in the next image, it decomposes the effect of the camera motion on the velocity vector of the target in the image frame. Based on the estimated velocity of the target and the image Jacobian, the control inputs of the mobile robot are determined in such a way that the target may appear inside the central area of the image frame. Since the shape of the target in the image frame varies due to rotation and translation of the target, a new shape adaptive Sum-of-Squared Difference (SSD) algorithm is proposed which uses the extended snake algorithm to extract the contour of the target and updates the template in every step of the matching process. The proposed scheme has been implemented using a Nomad Scout Robot II. The experimental results have shown that the proposed scheme follows the target within a negligible error range even when the target is temporarily lost due to various reasons. © 2005 Elsevier B.V. All rights reserved.
Keywords: Image based visual tracking; Mobile robot; Shape adaptive SSD algorithm; Extended snake algorithm
1. Introduction The function of tracking a target has become an essential one for surveillance systems and mobile robots. This function is used for automatically monitoring the motion of a suspicious person or an intruder in surveillance systems [1], and for following a target with various purposes in mobile robots [2]. Although many types of sensors have been used for implementing this function with their own advantages, vision system is most widely used for almost all applications because it can provide the information on the target with variant field of view or with different resolutions [3,4].
266
Y. Han, H. Hahn / Robotics and Autonomous Systems 53 (2005) 265–281
Tracking a target using a vision system can be classified into two cases: one is the case where the input device of the vision system (camera) has a fixed pose (position and orientation) and another one is the case where the camera can change its pose. When the pose of a camera is fixed, tracking a target is to locate the target in the sequence of input images [5,6]. In this case, the task of tracking a target becomes a problem of detecting and locating a target in the input image. If the pose of the camera can be changed, the problem becomes much more complex. In this case, tracking a target is not only to detect and locate the target in the input image but also to control the pose of the camera to keep the target inside the input image [7,8]. If the camera is located on a mobile robot (or a manipulator), its pose can be changed by controlling the pose of the mobile robot (or a manipulator). Controlling the pose of mobile robot using a vision system to track a target is called visual tracking. The tracking has either been performed in the image (image based visual servoing) or in the world coordinates (position based visual servoing). The position based one controls the robot using its positional information of the target represented in the 3D Cartesian space [9–11]. Since it uses the feature points of the target perspectively projected in the image frame of the camera to determine the relative pose of the target, it requires the information on the feature points of the target to be known a priori. To reduce the tracking error of a moving object, Saedan et al. [12] proposed the closed target pose estimation method. The control system consists of the visual control and the robot servo control. The visual control tolerates the errors due to camera calibration. On the
