C. Adaptive Template Matcher (ATM)As mentioned above, one of our constraints was to have a fully generic TLR. Therefore, and in order to be able to adapt our algorithm to different types of traffic lights, we designed Adaptive Templates. Those templates are evaluated with our Adaptive Template Matcher (ATM).Template Matching was used previously in different recognition process [11]-[2]. This technique is usually slow when applied on the whole image. However, we use the previously detected spots as hypothesis for template matching. Hence, we create candidates only where spots were detected. Those candidates will then be evaluated and either accepted or rejected according to their matching confidence value. The fact that we use the template matching only where spots were previously found, make it a lot faster (as detailed in the Results section).An Adaptive Template can be defined as a combination of the 2D visual shape representations of the 3D elements which form the real object. In addition, templates also define algorithmic operators linked to one or more elements and which will be evaluated at run time. Both elements and operators can have different weights and matching confidence thresholds, or even be set to non discriminatory. Therefore, if any non discriminatory element or operator failed, it prevents the candidate from being rejected.The matching process is the recursive evaluation of a template and its hierarchy, until all its elements and linked operators have been evaluated. The confidence value of an element is computed according to weight and confidence value of each child element or linked operator.Geometry Definition ATM uses the common top-down approach to define template geometry. This simple approach, shown in Fig. 5, involves decomposing a real 3D object (traffic light in our case) into 2D visual shapes.
C. Adaptive Template Matcher (ATM)<br><br>As mentioned above, one of our constraints was to have a fully generic TLR. Therefore, and in order to be able to adapt our algorithm to different types of traffic lights, we designed Adaptive Templates. Those templates are evaluated with our Adaptive Template Matcher (ATM).<br><br>Template Matching was used previously in different recognition process [11]-[2]. This technique is usually slow when applied on the whole image. However, we use the previously detected spots as hypothesis for template matching. Hence, we create candidates only where spots were detected. Those candidates will then be evaluated and either accepted or rejected according to their matching confidence value. The fact that we use the template matching only where spots were previously found, make it a lot faster (as detailed in the Results section).<br><br>An Adaptive Template can be defined as a combination of the 2D visual shape representations of the 3D elements which form the real object. In addition, templates also define algorithmic operators linked to one or more elements and which will be evaluated at run time. Both elements and operators can have different weights and matching confidence thresholds, or even be set to non discriminatory. Therefore, if any non discriminatory element or operator failed, it prevents the candidate from being rejected.<br><br>The matching process is the recursive evaluation of a template and its hierarchy, until all its elements and linked operators have been evaluated. The confidence value of an element is computed according to weight and confidence value of each child element or linked operator.<br><br>Geometry Definition ATM uses the common top-down approach to define template geometry. This simple approach, shown in Fig. 5, involves decomposing a real 3D object (traffic light in our case) into 2D visual shapes.
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