Abstract
One of the main challenges in automatic target tracking applications is represented by the need to maintain a low computational footprint, especially when dealing with real-time scenarios and the limited resources of embedded environments. In this context, significant results can be obtained by using forward-looking infrared sensors capable of providing distinctive features for targets of interest. In fact, due to their nature, forward-looking infrared (FLIR) images lend themselves to being used with extremely small footprint techniques based on the extraction of target intensity profiles. This work proposes a method for increasing the computational efficiency of template-based target tracking algorithms. In particular, the speed of the algorithm is improved by using a dynamic threshold that narrows the number of computations, thus reducing both execution time and resources usage. The proposed approach has been tested on several datasets, and it has been compared to several target tracking techniques. Gathered results, both in terms of theoretical analysis and experimental data, showed that the proposed approach is able to achieve the same robustness of reference algorithms by reducing the number of operations needed and the processing time.
Highlights
Detection and tracking of objects and people represent an important research topic in computer vision.The ever-increasing need for automatic, fast and reliable solutions for extracting information from video flows through image processing techniques are dictated by the large domain of vision-based applications.nowadays, a growing number of applications are envisioned to analyze the motion of pedestrians or moving objects in several scenarios, such as driver assistance, surveillance and human activity recognition, etc
Whenever an error condition is detected through the aforementioned metrics (Sections 3.2 and 3.3), a template matching (TM) phase is necessary to recover from the error in the target detection (TD) phase and to find the correct target position on the current frame
It is worth noticing that, in both formulas, a double contribution is considered: the first product takes into account intensity variation function (IVF) operations, whereas the second product is related to the TM phase
Summary
Detection and tracking of objects and people represent an important research topic in computer vision. Nowadays, a growing number of applications are envisioned to analyze the motion of pedestrians or moving objects in several scenarios, such as driver assistance (e.g., warning drivers about obstacles on the road, helping with the piloting of aircraft), surveillance and human activity recognition (e.g., locating pinpointing sources of ignition during firefighting operations, the control of servo-motor cameras in security areas), etc. From this point of view, image processing techniques are useful, among others, for tracking both vehicles (e.g., in automatic traffic monitoring tools) and people (e.g., for the detection of potentially dangerous situations).
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