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Target tracking is the key technology of many computer vision systems, and has important application value ina range of military and civil fields such as weapon guidance, intelligent transportation system, medical image system,virtual reality and so on. The essence of target tracking is to determine the position of the target in successive frames of avideo. Classic ideas of target tracking in videos are based on the surface model representing the target object, whichtranslates the tracking problem into the problem of maximizing the similarity coefficient between the model and thecandidate distribution. This kind of method is often with large computation complexity, and as for the platform withlimited resources, the robustness, accuracy and real-time performance are difficult to achieve a good balance. On theother side, tracking method based on decision model is the research hot spot in the field of target tracking. It treats thetarget localization as a binary classification problem, by designing classifiers to distinguish the target from the background effectively to achieve the aim of target tracking. This method is often able to achieve a higher frame rate, but inembedded system, relative research and applications are still very imperfect. Target tracking algorithms based on compress sensing in compression domain fuses these two types of target tracking methods, and often have low computational complexity, high accuracy and stability. However, there are still some problems to be solved in some applications. Thetarget tracking algorithm in compression domain based on compression perception is studied, and in order to meet thespecific application requirements, the shortcomings of original algorithm are improved. At the same time, based on thedesign idea and demand of miniaturized target position detector, a real-time image processing platform withTMS320DM6437 digital signal processor as the core is designed and implemented, and the algorithm is implementedand optimized on the DSP platform. The simulation and experiment results show that after the combination of Kalmanfilter, LBP feature and adding adaptive learning rate update strategy, the stability of the algorithm is improved. For theimplementation in DSP, after a series of optimizing measures, as for an image with resolution of 960 × 960, taking thetarget window of 80 × 80 into account, the computation speed can be up to 25 f/s, which can meet the requirement ofreal-time tracking. The embedded tracking system can track the selected moving objects continuously and stably, andcan meet the target localization and tracking requirements under specific applications, which has a real practical value.Morevoer, the method in this paper has a certain reference value for the research and applications of this kind of targettracking method in the embedded platform.
Feature compression.
LBP feature encoding.
Haar-like features based on different coding algorithm. (a) Haar-like features based on gray level. (b) Haar-like features based on LBP value.
Fusion of Kalman filter and original algorithm.
DSP algorithm flow diagram.
System hardware structure.
Video capture and display system.
Image buffers management.
DM6437 on-chip memory structure.
Time contrast sections consume.
David-indoor sequence test result. (a), (c): The 313th frame (a) and the 329th frame (c) before algorithm improvement. (b), (d): The 313th frame (b) and the 329th frame (d) after algorithm improvement.
Kite-surf sequence test result. (a), (c): The 38th frame (a) and the 65th frame (c) before algorithm improvement. (b), (d): The 38th frame (b) and the 65th frame (d) after algorithm improvement.
Box sequence test result. (a), (c): The 336th frame (a) and the 344th frame (c) before algorithm improvement. (b), (d): The 336th frame (b) and the 344th frame (d) after algorithm improvement.
Coke11 sequence test result. (a), (c): The 24th frame (a) and the 85th frame (c) before algorithm improvement. (b), (d): The 24th frame (b) and the 85th frame (d) after algorithm improvement.
Comparison of target tracking errors (before and after algorithm improvement). (a) Kite-surf sequence test result. (b) Bolt sequence test result.
DSP algorithm experiment result.