class_docstr: SiamRPN OPEBenchmark have eval_success, precision to select.
  eval_success is  distance between the center point of the predicted position
  precision is Compute overlap ratio between two rects through thresholds_overlap
  Parameters
  ----------
      dataset
          dataset Benchmark
class_docstr_tok: ["SiamRPN","OPEBenchmark","have","eval_success",",","precision","to","select",".","eval_success","is","distance","between","the","center","point","of","the","predicted","position","precision","is","Compute","overlap","ratio","between","two","rects","through","thresholds_overlap","Parameters","--","--","--","--","--","dataset","dataset","Benchmark"]
docstr: eval_success is  distance between the center point of the predicted position
     and the center position marked in the benchmark
  Parameters
  ----------
      eval_trackers: list of tracker name or single tracker name
  Return
  ----------
      return: dict of results
docstr_tok: ["eval_success","is","distance","between","the","center","point","of","the","predicted","position","and","the","center","position","marked","in","the","benchmark","Parameters","--","--","--","--","--","eval_trackers",":","list","of","tracker","name","or","single","tracker","name","Return","--","--","--","--","--","return",":","dict","of","results"]
code: def eval_success(self, eval_trackers=None):
      """eval_success is  distance between the center point of the predicted position
         and the center position marked in the benchmark
      Parameters
      ----------
          eval_trackers: list of tracker name or single tracker name
      Return
      ----------
          return: dict of results
      """
      if eval_trackers is None:
          eval_trackers = self.dataset.tracker_names
      if isinstance(eval_trackers, str):
          eval_trackers = [eval_trackers]
  
      success_ret = {}
      for tracker_name in eval_trackers:
          success_ret_ = {}
          for video in self.dataset:
              gt_traj = np.array(video.gt_traj)
              if tracker_name not in video.pred_trajs:
                  tracker_traj = video.load_tracker(
                      self.dataset.tracker_path, tracker_name, False
                  )
                  tracker_traj = np.array(tracker_traj)
              else:
                  tracker_traj = np.array(
                      video.pred_trajs[tracker_name]
                  )
              n_frame = len(gt_traj)
              if hasattr(video, "absent"):
                  gt_traj = gt_traj[video.absent == 1]
                  tracker_traj = tracker_traj[video.absent == 1]
              success_ret_[video.name] = success_overlap(
                  gt_traj, tracker_traj, n_frame
              )
          success_ret[tracker_name] = success_ret_
      return success_ret
  
file_path: gluon-cv/gluoncv/utils/metrics/tracking.py
imports: import numpy
  from ..filesystem import try_import_colorama
label_desc: Use image node_id 4 for calling the OPEBenchmark obj's underlying member method code with example usage: obj.eval_success(eval_trackers) and returns: success_ret