diff --git a/docs/advanced_tutorials/customization/action_recognotion/videobased_rec.md b/docs/advanced_tutorials/customization/action_recognotion/videobased_rec.md
index be260180b403d75b701f214ab096188f9d5b60c2..522eebe3d135a789a7843676ca477ec5e4b23a2c 100644
--- a/docs/advanced_tutorials/customization/action_recognotion/videobased_rec.md
+++ b/docs/advanced_tutorials/customization/action_recognotion/videobased_rec.md
@@ -130,3 +130,30 @@ python tools/export_model.py -c pptsm_fight_frames_dense.yaml \
                                 -p ppTSM_fight_best.pdparams \
                                 -o inference/ppTSM
 ```
+
+
+#### 推理可视化
+
+利用上步骤导出的模型，基于PaddleDetection中推理pipeline可完成自定义行为识别及可视化。
+
+新增行为后，需要对现有的可视化代码进行修改，目前代码支持打架二分类可视化，新增类别后需要根据识别结果自适应可视化推理结果。
+
+具体修改PaddleDetection中develop/deploy/pipeline/pipeline.py路径下PipePredictor类中visualize_video成员函数。当结果中存在'video_action'数据时，会对行为进行可视化。目前的逻辑是如果推理的类别为1，则为打架行为，进行可视化；否则不进行显示，即"video_action_score"为None。用户新增行为后，可根据类别index和对应的行为设置"video_action_text"字段，目前index=1对应"Fight"。相关代码块如下：
+
+```
+video_action_res = result.get('video_action')
+if video_action_res is not None:
+   video_action_score = None
+   if video_action_res and video_action_res["class"] == 1:
+         video_action_score = video_action_res["score"]
+   mot_boxes = None
+   if mot_res:
+         mot_boxes = mot_res['boxes']
+   image = visualize_action(
+         image,
+         mot_boxes,
+         action_visual_collector=None,
+         action_text="SkeletonAction",
+         video_action_score=video_action_score,
+         video_action_text="Fight")
+```