diff --git a/deploy/pphuman/README.md b/deploy/pphuman/README.md
index 2f5336d069052656e58a4f4ae8637e17ce84de00..baeb5a40ce828ba69464ee17930d421d844fe0f9 100644
--- a/deploy/pphuman/README.md
+++ b/deploy/pphuman/README.md
@@ -42,8 +42,10 @@ PP-Human提供了目标检测、属性识别、行为识别、ReID预训练模
| 任务 | 适用场景 | 精度 | 预测速度（ms） | 模型权重 | 预测部署模型 |
| :---------: |:---------: |:--------------- | :-------: | :------: | :------: |
-| 目标检测 | 图片输入 | mAP: 56.3 | 28.0ms |[下载链接](https://bj.bcebos.com/v1/paddledet/models/pipeline/mot_ppyoloe_l_36e_pipeline.pdparams) |[下载链接](https://bj.bcebos.com/v1/paddledet/models/pipeline/mot_ppyoloe_l_36e_pipeline.zip) |
-| 目标跟踪 | 视频输入 | MOTA: 72.0 | 33.1ms |[下载链接](https://bj.bcebos.com/v1/paddledet/models/pipeline/mot_ppyoloe_l_36e_pipeline.pdparams) |[下载链接](https://bj.bcebos.com/v1/paddledet/models/pipeline/mot_ppyoloe_l_36e_pipeline.zip) |
+| 目标检测(高精度) | 图片输入 | mAP: 56.6 | 28.0ms |[下载链接](https://bj.bcebos.com/v1/paddledet/models/pipeline/mot_ppyoloe_l_36e_pipeline.pdparams) |[下载链接](https://bj.bcebos.com/v1/paddledet/models/pipeline/mot_ppyoloe_l_36e_pipeline.zip) |
+| 目标检测(轻量级) | 图片输入 | mAP: 53.2 | 22.1ms |[下载链接](https://bj.bcebos.com/v1/paddledet/models/pipeline/mot_ppyoloe_s_36e_pipeline.pdparams) |[下载链接](https://bj.bcebos.com/v1/paddledet/models/pipeline/mot_ppyoloe_s_36e_pipeline.zip) |
+| 目标跟踪(高精度) | 视频输入 | MOTA: 79.5 | 33.1ms |[下载链接](https://bj.bcebos.com/v1/paddledet/models/pipeline/mot_ppyoloe_l_36e_pipeline.pdparams) |[下载链接](https://bj.bcebos.com/v1/paddledet/models/pipeline/mot_ppyoloe_l_36e_pipeline.zip) |
+| 目标跟踪(轻量级) | 视频输入 | MOTA: 69.1 | 27.2ms |[下载链接](https://bj.bcebos.com/v1/paddledet/models/pipeline/mot_ppyoloe_s_36e_pipeline.pdparams) |[下载链接](https://bj.bcebos.com/v1/paddledet/models/pipeline/mot_ppyoloe_s_36e_pipeline.zip) |
| 属性识别 | 图片/视频输入 属性识别 | mA: 94.86 | 单人2ms | - |[下载链接](https://bj.bcebos.com/v1/paddledet/models/pipeline/strongbaseline_r50_30e_pa100k.zip) |
| 关键点检测 | 视频输入 行为识别 | AP: 87.1 | 单人2.9ms |[下载链接](https://bj.bcebos.com/v1/paddledet/models/pipeline/dark_hrnet_w32_256x192.pdparams) |[下载链接](https://bj.bcebos.com/v1/paddledet/models/pipeline/dark_hrnet_w32_256x192.zip)
| 行为识别 | 视频输入 行为识别 | 准确率: 96.43 | 单人2.7ms | - |[下载链接](https://bj.bcebos.com/v1/paddledet/models/pipeline/STGCN.zip) |
diff --git a/deploy/pphuman/README_en.md b/deploy/pphuman/README_en.md
index cc165fd17c43bd61cf12c0ad3e5fddb57bee1f29..1138ae0f8e20148baffca42f0ef0577e6d8cf3f4 100644
--- a/deploy/pphuman/README_en.md
+++ b/deploy/pphuman/README_en.md
@@ -43,8 +43,10 @@ To make users have access to models of different scenarios, PP-Human provides pr
| Task | Scenario | Precision | Inference Speed（FPS） | Model Weights |Model Inference and Deployment |
| :---------: |:---------: |:--------------- | :-------: | :------: | :------: |
-| Object Detection | Image/Video Input | mAP: 56.3 | 28.0ms |[Link](https://bj.bcebos.com/v1/paddledet/models/pipeline/mot_ppyoloe_l_36e_pipeline.pdparams) |[Link](https://bj.bcebos.com/v1/paddledet/models/pipeline/mot_ppyoloe_l_36e_pipeline.zip) |
-| Object Tracking | Image/Video Input | MOTA: 72.0 | 33.1ms |[Link](https://bj.bcebos.com/v1/paddledet/models/pipeline/mot_ppyoloe_l_36e_pipeline.pdparams) |[Link](https://bj.bcebos.com/v1/paddledet/models/pipeline/mot_ppyoloe_l_36e_pipeline.zip) |
+| Object Detection(high-precision) | Image/Video Input | mAP: 56.6 | 28.0ms |[Link](https://bj.bcebos.com/v1/paddledet/models/pipeline/mot_ppyoloe_l_36e_pipeline.pdparams) |[Link](https://bj.bcebos.com/v1/paddledet/models/pipeline/mot_ppyoloe_l_36e_pipeline.zip) |
+| Object Detection(light-weight) | Image/Video Input | mAP: 53.2 | 22.1ms |[Link](https://bj.bcebos.com/v1/paddledet/models/pipeline/mot_ppyoloe_s_36e_pipeline.pdparams) |[Link](https://bj.bcebos.com/v1/paddledet/models/pipeline/mot_ppyoloe_s_36e_pipeline.zip) |
+| Object Tracking(high-precision) | Image/Video Input | MOTA: 79.5 | 33.1ms |[Link](https://bj.bcebos.com/v1/paddledet/models/pipeline/mot_ppyoloe_l_36e_pipeline.pdparams) |[Link](https://bj.bcebos.com/v1/paddledet/models/pipeline/mot_ppyoloe_l_36e_pipeline.zip) |
+| Object Tracking(light-weight) | Image/Video Input | MOTA: 69.1 | 27.2ms |[Link](https://bj.bcebos.com/v1/paddledet/models/pipeline/mot_ppyoloe_s_36e_pipeline.pdparams) |[Link](https://bj.bcebos.com/v1/paddledet/models/pipeline/mot_ppyoloe_s_36e_pipeline.zip) |
| Attribute Recognition | Image/Video Input Attribute Recognition | mA: 94.86 | 2ms per person | - |[Link](https://bj.bcebos.com/v1/paddledet/models/pipeline/strongbaseline_r50_30e_pa100k.zip) |
| Keypoint Detection | Video Input Falling Recognition | AP: 87.1 | 2.9ms per person | [Link](https://bj.bcebos.com/v1/paddledet/models/pipeline/dark_hrnet_w32_256x192.pdparams) |[Link](https://bj.bcebos.com/v1/paddledet/models/pipeline/dark_hrnet_w32_256x192.zip)
| Falling Recognition | Video Input Falling Recognition | Precision 96.43 | 2.7ms per person | - |[Link](https://bj.bcebos.com/v1/paddledet/models/pipeline/STGCN.zip) |
diff --git a/deploy/pphuman/config/tracker_config.yml b/deploy/pphuman/config/tracker_config.yml
index ddd55e8653870ed9bdfe9734995e8af5b56f49e2..50e92f7d7aecf94534c406667347cc0f45681966 100644
--- a/deploy/pphuman/config/tracker_config.yml
+++ b/deploy/pphuman/config/tracker_config.yml
@@ -2,7 +2,7 @@
# The tracker of MOT JDE Detector (such as FairMOT) is exported together with the model.
# Here 'min_box_area' and 'vertical_ratio' are set for pedestrian, you can modify for other objects tracking.
-type: JDETracker # 'JDETracker' or 'DeepSORTTracker'
+type: JDETracker
# BYTETracker
JDETracker:
@@ -13,14 +13,3 @@ JDETracker:
match_thres: 0.9
min_box_area: 0
vertical_ratio: 0 # 1.6 for pedestrian
-
-DeepSORTTracker:
- input_size: [64, 192]
- min_box_area: 0
- vertical_ratio: -1
- budget: 100
- max_age: 70
- n_init: 3
- metric_type: cosine
- matching_threshold: 0.2
- max_iou_distance: 0.9
diff --git a/deploy/pphuman/docs/mot.md b/deploy/pphuman/docs/mot.md
index acfed453ab3c9becf4254f12d24821caeaa44d36..893ac479cb1a7b697fffbab9508c2a6960feecfe 100644
--- a/deploy/pphuman/docs/mot.md
+++ b/deploy/pphuman/docs/mot.md
@@ -6,9 +6,10 @@
| 任务 | 算法 | 精度 | 预测速度(ms) |下载链接 |
|:---------------------|:---------:|:------:|:------:| :---------------------------------------------------------------------------------: |
-| 行人检测/跟踪 | PP-YOLOE | mAP: 56.3
MOTA: 72.0 | 检测: 28ms
跟踪：33.1ms | [下载链接](https://bj.bcebos.com/v1/paddledet/models/pipeline/mot_ppyoloe_l_36e_pipeline.zip) |
+| 行人检测/跟踪 | PP-YOLOE-l | mAP: 56.6
MOTA: 79.5 | 检测: 28.0ms
跟踪：33.1ms | [下载链接](https://bj.bcebos.com/v1/paddledet/models/pipeline/mot_ppyoloe_l_36e_pipeline.zip) |
+| 行人检测/跟踪 | PP-YOLOE-s | mAP: 53.2
MOTA: 69.1 | 检测: 22.1ms
跟踪：27.2ms | [下载链接](https://bj.bcebos.com/v1/paddledet/models/pipeline/mot_ppyoloe_s_36e_pipeline.zip) |
-1. 检测/跟踪模型精度为MOT17，CrowdHuman，HIEVE和部分业务数据融合训练测试得到
+1. 检测/跟踪模型精度为[COCO-Person](http://cocodataset.org/), [CrowdHuman](http://www.crowdhuman.org/), [HIEVE](http://humaninevents.org/) 和部分业务数据融合训练测试得到，验证集为业务数据
2. 预测速度为T4 机器上使用TensorRT FP16时的速度, 速度包含数据预处理、模型预测、后处理全流程
## 使用方法
@@ -53,8 +54,8 @@ python deploy/pphuman/pipeline.py --config deploy/pphuman/config/infer_cfg.yml \
## 方案说明
-1. 目标检测/多目标跟踪获取图片/视频输入中的行人检测框，模型方案为PP-YOLOE，详细文档参考[PP-YOLOE](../../../configs/ppyoloe/README_cn.md)
-2. 多目标跟踪模型方案基于[ByteTrack](https://arxiv.org/pdf/2110.06864.pdf)，采用PP-YOLOE替换原文的YOLOX作为检测器，采用BYTETracker作为跟踪器。
+1. 目标检测/多目标跟踪获取图片/视频输入中的行人检测框，模型方案为PP-YOLOE，详细文档参考[PP-YOLOE](../../../configs/ppyoloe)
+2. 多目标跟踪模型方案基于[ByteTrack](https://arxiv.org/pdf/2110.06864.pdf)，采用PP-YOLOE替换原文的YOLOX作为检测器，采用BYTETracker作为跟踪器，详细文档参考[ByteTrack](../../../configs/mot/bytetrack)
## 参考文献
```
diff --git a/deploy/pphuman/docs/mot_en.md b/deploy/pphuman/docs/mot_en.md
index 61a970898dc8ceab27c5d1b38782d44efbe961a2..510d86c0605d487358d90e5fa0f0ad47b4651512 100644
--- a/deploy/pphuman/docs/mot_en.md
+++ b/deploy/pphuman/docs/mot_en.md
@@ -6,9 +6,10 @@ Pedestrian detection and tracking is widely used in the intelligent community, i
| Task | Algorithm | Precision | Inference Speed(ms) | Download Link |
|:---------------------|:---------:|:------:|:------:| :---------------------------------------------------------------------------------: |
-| Pedestrian Detection/ Tracking | PP-YOLOE | mAP: 56.3
MOTA: 72.0 | Detection: 28ms
Tracking：33.1ms | [Download Link](https://bj.bcebos.com/v1/paddledet/models/pipeline/mot_ppyoloe_l_36e_pipeline.zip) |
+| Pedestrian Detection/ Tracking | PP-YOLOE-l | mAP: 56.6
MOTA: 79.5 | Detection: 28.0ms
Tracking：33.1ms | [Download Link](https://bj.bcebos.com/v1/paddledet/models/pipeline/mot_ppyoloe_l_36e_pipeline.zip) |
+| Pedestrian Detection/ Tracking | PP-YOLOE-s | mAP: 53.2
MOTA: 69.2 | Detection: 22.1ms
Tracking：27.2ms | [Download Link](https://bj.bcebos.com/v1/paddledet/models/pipeline/mot_ppyoloe_s_36e_pipeline.zip) |
-1. The precision of the pedestrian detection/ tracking model is obtained by trainning and testing on [MOT17](https://motchallenge.net/), [CrowdHuman](http://www.crowdhuman.org/), [HIEVE](http://humaninevents.org/) and some business data.
+1. The precision of the pedestrian detection/ tracking model is obtained by trainning and testing on [COCO-Person](http://cocodataset.org/), [CrowdHuman](http://www.crowdhuman.org/), [HIEVE](http://humaninevents.org/) and some business data.
2. The inference speed is the speed of using TensorRT FP16 on T4, the total number of data pre-training, model inference, and post-processing.
## How to Use
@@ -57,7 +58,7 @@ Data source and copyright owner：Skyinfor Technology. Thanks for the provision
1. Get the pedestrian detection box of the image/ video input through object detection and multi-object tracking. The adopted model is PP-YOLOE, and for details, please refer to [PP-YOLOE](../../../configs/ppyoloe).
-2. The multi-object tracking model solution is based on [ByteTrack](https://arxiv.org/pdf/2110.06864.pdf), and replace the original YOLOX with P-YOLOE as the detector，and BYTETracker as the tracker.
+2. The multi-object tracking model solution is based on [ByteTrack](https://arxiv.org/pdf/2110.06864.pdf), and replace the original YOLOX with P-YOLOE as the detector，and BYTETracker as the tracker, please refer to [ByteTrack](../../../configs/mot/bytetrack).
## Reference
```