diff --git a/deploy/pipeline/docs/tutorials/PPHuman_QUICK_STARTED.md b/deploy/pipeline/docs/tutorials/PPHuman_QUICK_STARTED.md
index 245db50b6a9d4d58066281fe7c70c69b92f89e80..0e1ddb92c7b9c733b6f7f61679cee35b9edf3450 100644
--- a/deploy/pipeline/docs/tutorials/PPHuman_QUICK_STARTED.md
+++ b/deploy/pipeline/docs/tutorials/PPHuman_QUICK_STARTED.md
@@ -129,6 +129,12 @@ python deploy/pipeline/pipeline.py --config deploy/pipeline/config/infer_cfg_pph
 python deploy/pipeline/pipeline.py --config deploy/pipeline/config/infer_cfg_pphuman.yml --video_dir=mtmct_dir/ --device=gpu [--run_mode trt_fp16]
 ```
 
+对rtsp流的支持，video_file后面的视频地址更换为rtsp流地址，示例如下：
+```
+# 行人属性识别，指定配置文件路径和测试视频，在配置文件```deploy/pipeline/config/infer_cfg_pphuman.yml```中的ATTR部分enable设置为```True```
+python deploy/pipeline/pipeline.py --config deploy/pipeline/config/infer_cfg_pphuman.yml -o visual=False --video_file=rtsp://[YOUR_RTSP_SITE] --device=gpu [--run_mode trt_fp16]
+```
+
 ### 参数说明
 
 | 参数 | 是否必须|含义 |
@@ -137,7 +143,7 @@ python deploy/pipeline/pipeline.py --config deploy/pipeline/config/infer_cfg_pph
 | --model_dir | Option | PP-Human中各任务模型路径，优先级高于配置文件, 例如`--model_dir det=better_det/ attr=better_attr/`|
 | --image_file | Option | 需要预测的图片 |
 | --image_dir  | Option |  要预测的图片文件夹路径   |
-| --video_file | Option | 需要预测的视频 |
+| --video_file | Option | 需要预测的视频，或者rtsp流地址 |
 | --camera_id | Option | 用来预测的摄像头ID，默认为-1(表示不使用摄像头预测，可设置为：0 - (摄像头数目-1) )，预测过程中在可视化界面按`q`退出输出预测结果到：output/output.mp4|
 | --device | Option | 运行时的设备，可选择`CPU/GPU/XPU`，默认为`CPU`|
 | --output_dir | Option|可视化结果保存的根目录，默认为output/|
diff --git a/deploy/pipeline/docs/tutorials/PPVehicle_QUICK_STARTED.md b/deploy/pipeline/docs/tutorials/PPVehicle_QUICK_STARTED.md
index e559ef3087cb687baddbe011175f22b3705cc583..aa228337ac7614dfc263c3df484e770caee28043 100644
--- a/deploy/pipeline/docs/tutorials/PPVehicle_QUICK_STARTED.md
+++ b/deploy/pipeline/docs/tutorials/PPVehicle_QUICK_STARTED.md
@@ -84,6 +84,12 @@ python deploy/pipeline/pipeline.py --config deploy/pipeline/config/infer_cfg_ppv
 
 ```
 
+对rtsp流的支持，video_file后面的视频地址更换为rtsp流地址，示例如下：
+```
+# 车辆属性识别，指定配置文件路径和测试视频，在配置文件```deploy/pipeline/config/infer_cfg_ppvehicle.yml```中的ATTR部分enable设置为```True```
+python deploy/pipeline/pipeline.py --config deploy/pipeline/config/infer_cfg_ppvehicle.yml  -o visual=False --video_file=rtsp://[YOUR_RTSP_SITE] --device=gpu [--run_mode trt_fp16]
+```
+
 ### 参数说明
 
 | 参数 | 是否必须|含义 |
@@ -92,7 +98,7 @@ python deploy/pipeline/pipeline.py --config deploy/pipeline/config/infer_cfg_ppv
 | --model_dir | Option | 各任务模型路径，优先级高于配置文件, 例如`--model_dir det=better_det/ attr=better_attr/`|
 | --image_file | Option | 需要预测的图片 |
 | --image_dir  | Option |  要预测的图片文件夹路径   |
-| --video_file | Option | 需要预测的视频 |
+| --video_file | Option | 需要预测的视频，或者rtsp流地址 |
 | --camera_id | Option | 用来预测的摄像头ID，默认为-1(表示不使用摄像头预测，可设置为：0 - (摄像头数目-1) )，预测过程中在可视化界面按`q`退出输出预测结果到：output/output.mp4|
 | --device | Option | 运行时的设备，可选择`CPU/GPU/XPU`，默认为`CPU`|
 | --output_dir | Option|可视化结果保存的根目录，默认为output/|
diff --git a/deploy/pipeline/pipeline.py b/deploy/pipeline/pipeline.py
index fe3654dae23620a4f40b25ff7431ed34243e40a7..7683da175b86dfbe429063b28c2438b7aa0e7ad2 100644
--- a/deploy/pipeline/pipeline.py
+++ b/deploy/pipeline/pipeline.py
@@ -130,7 +130,9 @@ class Pipeline(object):
             self.multi_camera = False
 
         elif video_file is not None:
-            assert os.path.exists(video_file), "video_file not exists."
+            assert os.path.exists(
+                video_file
+            ) or 'rtsp' in video_file, "video_file not exists and not an rtsp site."
             self.multi_camera = False
             input = video_file
             self.is_video = True
@@ -659,6 +661,8 @@ class PipePredictor(object):
         # mot -> pose -> action
         capture = cv2.VideoCapture(video_file)
         video_out_name = 'output.mp4' if self.file_name is None else self.file_name
+        if "rtsp" in video_file:
+            video_out_name = video_out_name + "_rtsp.mp4"
 
         # Get Video info : resolution, fps, frame count
         width = int(capture.get(cv2.CAP_PROP_FRAME_WIDTH))
@@ -767,8 +771,10 @@ class PipePredictor(object):
                 if self.with_vehicleplate:
                     if frame_id > self.warmup_frame:
                         self.pipe_timer.module_time['vehicleplate'].start()
+                    plate_input, _, _ = crop_image_with_mot(
+                        frame_rgb, mot_res, expand=False)
                     platelicense = self.vehicleplate_detector.get_platelicense(
-                        crop_input)
+                        plate_input)
                     if frame_id > self.warmup_frame:
                         self.pipe_timer.module_time['vehicleplate'].end()
                     self.pipeline_res.update(platelicense, 'vehicleplate')
diff --git a/deploy/pipeline/ppvehicle/vehicle_plate.py b/deploy/pipeline/ppvehicle/vehicle_plate.py
index 42e5ba4b0a1cb1bfcd7fb1422cf8b700d904294c..6a41be6f1ecd92da842cc7006bedad3e372ae92c 100644
--- a/deploy/pipeline/ppvehicle/vehicle_plate.py
+++ b/deploy/pipeline/ppvehicle/vehicle_plate.py
@@ -268,7 +268,7 @@ class PlateRecognizer(object):
             platelicense = ""
             for text_info in text_pcar:
                 text = text_info[0][0][0]
-                if len(text) > 2 and text[0] in simcode and len(text) < 10:
+                if len(text) > 2 and len(text) < 10:
                     platelicense = text
             plate_all["plate"].append(platelicense)
         return plate_all
diff --git a/deploy/python/visualize.py b/deploy/python/visualize.py
index 24aa40796f7ef9ae4d55df46f9a6223119183bec..626da02555985a5568f3bdaff20705d8a8dd1c11 100644
--- a/deploy/python/visualize.py
+++ b/deploy/python/visualize.py
@@ -418,7 +418,7 @@ def visualize_vehicleplate(im, results, boxes=None):
         im = np.ascontiguousarray(np.copy(im))
 
     im_h, im_w = im.shape[:2]
-    text_scale = max(0.5, im.shape[0] / 3000.)
+    text_scale = max(1.0, im.shape[0] / 1600.)
     text_thickness = 1
 
     line_inter = im.shape[0] / 40.