diff --git a/modelscope/models/cv/video_single_object_tracking/utils/utils.py b/modelscope/models/cv/video_single_object_tracking/utils/utils.py
index 752ec272..90513a2a 100644
--- a/modelscope/models/cv/video_single_object_tracking/utils/utils.py
+++ b/modelscope/models/cv/video_single_object_tracking/utils/utils.py
@@ -238,3 +238,10 @@ def check_box(box: list, image_height, image_width) -> bool:
     if box[3] < 0 or box[3] >= image_height:
         return False
     return True
+
+
+def timestamp_format(seconds):
+    m, s = divmod(seconds, 60)
+    h, m = divmod(m, 60)
+    time = '%02d:%02d:%06.3f' % (h, m, s)
+    return time
diff --git a/modelscope/outputs.py b/modelscope/outputs.py
index 92e3410b..b96f38d3 100644
--- a/modelscope/outputs.py
+++ b/modelscope/outputs.py
@@ -247,9 +247,11 @@ TASK_OUTPUTS = {
     #               [x1, y1, x2, y2],
     #               [x1, y1, x2, y2],
     #               [x1, y1, x2, y2],
-    #             ]
+    #             ],
+    #   "timestamps": ["hh:mm:ss", "hh:mm:ss", "hh:mm:ss"]
     # }
-    Tasks.video_single_object_tracking: [OutputKeys.BOXES],
+    Tasks.video_single_object_tracking:
+    [OutputKeys.BOXES, OutputKeys.TIMESTAMPS],
 
     # live category recognition result for single video
     # {
diff --git a/modelscope/pipelines/cv/video_single_object_tracking_pipeline.py b/modelscope/pipelines/cv/video_single_object_tracking_pipeline.py
index c47fc15f..4169def7 100644
--- a/modelscope/pipelines/cv/video_single_object_tracking_pipeline.py
+++ b/modelscope/pipelines/cv/video_single_object_tracking_pipeline.py
@@ -9,8 +9,8 @@ from modelscope.models.cv.video_single_object_tracking.config.ostrack import \
     cfg
 from modelscope.models.cv.video_single_object_tracking.tracker.ostrack import \
     OSTrack
-from modelscope.models.cv.video_single_object_tracking.utils.utils import \
-    check_box
+from modelscope.models.cv.video_single_object_tracking.utils.utils import (
+    check_box, timestamp_format)
 from modelscope.outputs import OutputKeys
 from modelscope.pipelines.base import Input, Pipeline
 from modelscope.pipelines.builder import PIPELINES
@@ -45,7 +45,10 @@ class VideoSingleObjectTrackingPipeline(Pipeline):
 
     def forward(self, input: Input) -> Dict[str, Any]:
         output_boxes = []
+        output_timestamps = []
         cap = cv2.VideoCapture(self.video_path)
+        fps = cap.get(cv2.CAP_PROP_FPS)
+        frame_idx = 0
         success, frame = cap.read()
         if success is False:
             raise Exception(
@@ -58,6 +61,7 @@ class VideoSingleObjectTrackingPipeline(Pipeline):
             raise Exception('modelscope error: init_box out of image range ',
                             init_box)
         output_boxes.append(init_box.copy())
+        output_timestamps.append(timestamp_format(seconds=frame_idx / fps))
         init_box[2] = init_box[2] - init_box[0]
         init_box[3] = init_box[3] - init_box[1]
         self.tracker.initialize(frame, {'init_bbox': init_box})
@@ -67,14 +71,17 @@ class VideoSingleObjectTrackingPipeline(Pipeline):
             ret, frame = cap.read()
             if frame is None:
                 break
+            frame_idx += 1
             out = self.tracker.track(frame)
             state = [int(s) for s in out['target_bbox']]
             output_boxes.append(state)
+            output_timestamps.append(timestamp_format(seconds=frame_idx / fps))
         cap.release()
         logger.info('tracking process done')
 
         return {
             OutputKeys.BOXES: output_boxes,
+            OutputKeys.TIMESTAMPS: output_timestamps
         }
 
     def postprocess(self, inputs: Dict[str, Any]) -> Dict[str, Any]: