## 1 Introduction  
Imaging techniques including photography, cinematography and analog/digital video techniques are always favorite tools for research scientists to investigate motion of fluids and solid objects. For example, one of the imaging techniques, i.e. the particle image velocimetry (PIV), are widely used to measure complex fluid flows and quantify motion of large solid particles and bubbles in fluids [1; 2]. Recent advances in high-speed digital imaging system and digital image evaluation algorithm have made it possible to measure mini- and micro-scale motion of insect body parts with high accuracy and high temporal resolution. The time resolved quantification of insect body part motion is of great interest because the insect body part (head, wing, antenna, hairs etc.) motion is usually related to the communication between insects or the reaction of insects under environment excitations. Currently, some commercially available high-speed imaging systems are capable of capturing more than 10,000 frames per second (fps) with sufficient digital resolution, e.g. 256\(\times\)256 pixels, with which an image pattern of 32\(\times\)32 pixels can be tracked in a displacement range of up to 200 pixels. According to publications on the particle image velocimetry (PIV), i.e. an image pattern tracking technique for fluid flow measurements, the uncertainties for determining the displacement of image pattern of 32\(\times\)32 pixels may be less than 0.1 pixels [3-7] that ensures the high accuracy of determining the insect body part motion. In addition, by using a high-speed imaging system, thousands of image frames can be consecutively captured with a constant time interval between frames, so that a temporal analysis of the inset body motion can be conducted. Most of the particle image pattern tracking algorithms in PIV can directly be used to track the image pattern of insect body part for determining the displacement between two or a few more frames. Difficulties arise when tracking the image pattern of insect body part among hundreds to thousands of image frames. At first, as mentioned in many publications on PIV, the most PIV algorithms were designed to track a group of image patterns of very small particles with an optimal particle image diameter around 3 pixels [8]; however, the equivalent diameter of the tracked insect image pattern is usually more than 10 pixels. The algorithm for the insect body motion tracking should be insensitive to the image pattern size or should work well for larger particle images. The second difficulty is that an insect body part can not be assumed as a point light source like a tracer particle in PIV experiments. The image pattern of an insect body part may have a complex structure, may rotate in the objective plane and may deform because of the off-plane motion. The image pattern distortion were considered in PIV algorithms by using particle image displacements near to the tracked image pattern [9-11], but those ideas cannot be applied in the insect body part tracking because no neighborhood information is available to correct the distorted image pattern of the insect body part. It seems that the only effective way to solve the problem of strong image pattern distortion is to use a high frame rate so that the image pattern distortion between two neighbored frames may be small enough. When tracking the image pattern among a great number of image frames, the bias errors will be accumulated frame by frame so that the tracked image pattern may deviate from the originally chosen one, and consequently, a false tracking result may finally be resulted in. Therefore, the algorithm for the insect body motion should have a very low bias error level, and the bias error should properly be controlled to ensure that the tracking procedure can be completed with high reliability.