This paper discusses the problem of task selection in active-vision systems. It is shown that optimization of motion perceptibility does not work as the primary task. On the other hand, it is demonstrated that optimization of motion perceptibility under target tracking produces reasonable camera motion. The perceptibility measure is induced by a certain Jacobian matrix and not by the interaction matrix. The interaction matrix does not produce cooperative behavior of multiple active cameras.

This paper discusses the task selection problem for active vision systems, that is, what tasks should be selected to control active vision systems and which order of priority should be set for the selected tasks. One possible task to determine an optimal camera placement is to obtain high resolvability, or equivalently, to optimize a perceptibility measure which is a quantitative scaling measure from error of the measured velocity of image features in the image plane to error of the corresponding velocity computed in the camera coordinate frame. This paper first shows that optimization of the perceptibility measure may produce unreasonable motion responses for active vision systems, and it should not be selected for a primary task to control active vision systems. This paper then proposes target tracking as a primary task and optimization of a perceptibility measure as a secondary task. The perceptibility measure for the secondary task is induced by certain Jacobian matrices, not the image Jacobian matrix, to produce cooperative behavior for active vision systems with multiple cameras.

This paper proposes a visual tracking method which is robust to occlusion. This paper also integrates the visual tracking method and visual servo control into a vision-based control method with occlusion handling. The proposed method chooses a set of correctly extracted image features, and it then obtains an estimate of all the image features from the correctly extracted image features. The estimation procedure makes it possible to track image features even when occlusion occurs. The method has low computational complexity, since the image Jacobian is used for the image feature selection and estimation. In addition, even when the controller fails to track a moving image feature, it can find the failed image feature without a global search over the entire image plane. As a result, it can track the failed image feature again quickly.

This paper proposes an assistant and training system for controlling an unmanned helicopter. The unmanned helicopter does not have any sensors which measure its position or posture. Stationary cameras are placed on the flight field. The helicopter is controlled by using a visual servo technique as follows. An operator steers the helicopter using sticks on a hand-held input device. The sticks make reference signals. The assistant system designs control signals such that the helicopter tracks the reference signals. The proposed system has the following four functions: automatic takeoff and landing, control channel selection, flight in a desired area, and automatic motion generation. They enables beginners to control an unmanned helicopter. The system provides real actions of unmanned helicopters. This is the main difference from flight simulators.

This paper proposes a switched visual feedback control method for a micro helicopter under occlusions. Two stationary cameras are placed on the ground. They track four black balls attached to rods connected to the bottom of the helicopter. The control input is computed by using the errors between the positions of the tracked objects and pre-specifled reference values for them. The multi-camera configuration is redundant for helicopter control, but it enables us to design a switched controller which is robust against occlusions. An occlusion occurs when an object moves across in front of a camera or when the background color happens to be similar to the color of a tracked object. Multi-camera systems are suitable for designing a robust controller under occlusions, since even when a tracked object is not visible in a camera view, other cameras may track it. The authors have proposed a camera selection approach: If an occlusion is detected in a camera view then the other camera is used to control the helicopter. This paper presents another switched visual feedback control method for a micro helicopter under occlusions. This is called the image feature selection approach. This paper assumes that at most one tracked object is occluded at each time to simplify notations, although we can draw general conclusions. The errors between the positions of the tracked objects and pre-specifled references are used to compute the control input, when all the tracked objects are visible. If one of the tracked objects is invisible, then the controller uses the errors given by the other three tracked objects. The position of the occluded object is also estimated by using the other three tracked objects.