## 5 Summary and conclusion  
The presented method consists of a high-speed digital imaging system and a multi-pass, correlation-based image pattern tracking algorithm. The high-speed digital imaging system resolves strong distortion of insect image pattern with enough number of image frames and enables a time resolved analysis of insect body part motion. The described image pattern tracking algorithm is developed on the basis of the correlation-based interrogation with continuous window-shift (CCWS), which is an algorithm for digital PIV recording evaluation. Since the CCWS algorithm has very low bias error  
Figure 16: Frequency response of an antennal hair of a fire ant to a vibration signal of 700 Hz.  
level and is insensitive to the image pattern size, it makes tracking of complex image patterns possible among hundreds and even thousands of successively recorded frames.  
Tests with synthetic image patterns demonstrate that a proper selection of the reference image pattern is very important for an image pattern tracking. When a fixed reference pattern is used, i.e. it is chosen in the first frame and does not change anymore, there will be a large error for determining the image pattern displacement between frames, and consequently, the instantaneous insect body moving velocity cannot be measured at high accuracy. On the other hand, when the tracking is conducted at the maximal image pattern freedom, i.e. the reference image pattern for tracking in the next frame is replaced with every tracked image pattern; a large error will appear by determining the image pattern position. Therefore, an optimal image pattern freedom limit should be setup for the image pattern tracking to avoid the large errors for determining both position and displacement of the tracked image pattern. The optimal freedom limit can be determined with a multi-pass test on the accumulated bias at a selected image frame in the following way: (1) At first, a time period is select that starts at the first frame and includes frames of the most strong image pattern distortion, and the last frame in the selected period should have the most similar image pattern as the first frame. (2) Then, the image pattern tracking runs with increasing freedom limit from \(L\)=1 with step 1. (3) When the accumulated bias frame begins to rise, it is time to stop the test runs. (4) Finally, the freedom limit \(L\) is chosen with the minimal accumulated bias.  
The described algorithm has been successfully applied to investigate the head-banging behavior of a termite. The position and velocity of the termite head in a head-banging period was determined in the camera focus plane with a high temporal resolution. The acceleration time history was deduced by post processing of the velocity time history, so that the striking force of a termite head-bang on the substrate was estimated. The application for the fire ant antenna in sound waves demonstrates that the described algorithm can be used to detect micro scale, high frequency vibration of an inset body part.  
The authors would like to state here that the presented algorithm is one out of several effective algorithms for image pattern tracking, and others may be more suitable for the presented application cases. The authors will try to conducted comparison of possible alternative algorithms in the near future. It should also be mentioned that image tracking of solid objects are much different from tracking tracer particles in fluid flows with particle image velocimetry (PIV), for instance, window deformation and rotation methods in PIV require known particle image displacements at several neighbored evaluation points, so that they cannot be used for the presented cases. Therefore, a high-speed imaging system is essential with the presented algorithm to resolve rotation and deformation of the tracked image pattern.