function res = blockedImageAutomationAlgorithm(algObj,bstruct)
   % Detect people using aggregate channel features
   detector = peopleDetectorACF('inria-100x41');
   [bboxes, scores] = detect(detector, bstruct.Data,...
            'SelectStrongest', false);
           
   % Apply non-maximum suppression to select the strongest bounding boxes.
   [selectedBboxes, selectedScores] = selectStrongestBbox(bboxes, scores,...
            'RatioType', 'Min',...
            'OverlapThreshold', 0.65);
           
   % Consider only detections that meet specified score threshold
   selectedBboxes = selectedBboxes(selectedScores > 0, :);
   selectedBboxes(:,1) = selectedBboxes(:,1)+bstruct.Start(2);
   selectedBboxes(:,2) = selectedBboxes(:,2)+bstruct.Start(1);
           
   if algObj.SelectedLabelDefinitions.Type == "Rectangle"
      % Add the selected label at the bounding box position(s)
      res = struct(...
          'Type', labelType.Rectangle,...
          'Name', algObj.SelectedLabelDefinitions.Name,...
          'Position', selectedBboxes);
   end
end