Camera calibration is one of the most basic and important processes in optical measuring field. Generally, the objective
of camera calibration is to estimate the internal and external parameters of object cameras, while the orientation error of
optical axis is not included yet. Orientation error of optical axis is a important factor, which seriously affects measuring
precision in high-precision measurement field, especially for those distant aerospace measurement in which object
distance is much longer than focal length, that lead to magnifying the orientation errors to thousands times. In order to
eliminate the influence of orientation error of camera optical axis, the imaging model of camera is analysed and
established in this paper, and the calibration method is also introduced: Firstly, we analyse the reasons that cause optical
axis error and its influence. Then, we find the model of optical axis orientation error and imaging model of camera
basing on it’s practical physical meaning. Furthermore, we derive the bundle adjustment algorithm which could compute
the internal and external camera parameters and absolute orientation of camera optical axis simultaneously at high
precision. In numeric simulation, we solve the camera parameters by using bundle adjustment optimization algorithm,
then we correct the image points by calibration results according to the model of optical axis error, and the simulation
result shows that our calibration model is reliable, effective and precise.
An improved online long-term visual tracking algorithm, named adaptive and accelerated TLD (AA-TLD) based on
Tracking-Learning-Detection (TLD) which is a novel tracking framework has been introduced in this paper. The
improvement focuses on two aspects, one is adaption, which makes the algorithm not dependent on the pre-defined
scanning grids by online generating scale space, and the other is efficiency, which uses not only algorithm-level
acceleration like scale prediction that employs auto-regression and moving average (ARMA) model to learn the object
motion to lessen the detector’s searching range and the fixed number of positive and negative samples that ensures a
constant retrieving time, but also CPU and GPU parallel technology to achieve hardware acceleration. In addition, in
order to obtain a better effect, some TLD’s details are redesigned, which uses a weight including both normalized
correlation coefficient and scale size to integrate results, and adjusts distance metric thresholds online. A contrastive
experiment on success rate, center location error and execution time, is carried out to show a performance and efficiency
upgrade over state-of-the-art TLD with partial TLD datasets and Shenzhou IX return capsule image sequences. The
algorithm can be used in the field of video surveillance to meet the need of real-time video tracking.
Measuring the three-dimension (3D) orientation parameters of the axis symmetry objects, such as missile and rocket,
plays an important role of optimization design and malfunction analysis of the targets in shooting range experimentations.
If the target is clear-cut, there are already many ways to extract the axis precisely, and get the three-dimension orientation
parameters by triangulation method. But in practical experimentations, we sometimes face the problem that the target is
illuminated by intense sunlight, which with the background of sky will cause the imprecision of axis extraction and the
increasing of measurement error. To solve the problem, this paper presents an accurate method to extract the target’s axis.
The method build a point set, and try to put all the target’s points into it by using the priori-knowledge, such as the
symmetry target’s characteristic under unilateral illumination condition, then calculate the regional minimum inertia axis
of the set’s points and get the target’s axis. Experimental results show that this method is efficient and robust to noise,
which can meet the requirement of unilaterally illuminated target 3D pose interpretation in shooting range.