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‎LICENSE

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‎README.md

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# CMU Object Detection & Tracking for Surveillance Video Activity Detection
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This repository contains the code and models for object detection and tracking from the CMU [DIVA](https://www.iarpa.gov/index.php/research-programs/diva) system. Our system (INF & MUDSML) achieves the best performance on the ActEv (leaderboard)[https://actev.nist.gov/prizechallenge#tab_leaderboard].
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If you find this code useful in your research then please cite
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```
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@inproceedings{chen2019minding,
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title={Minding the Gaps in a Video Action Analysis Pipeline},
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author={Chen, Jia and Liu, Jiang and Liang, Junwei and Hu, Ting-Yao and Ke, Wei and Barrios, Wayner and Huang, Dong and Hauptmann, Alexander G},
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booktitle={2019 IEEE Winter Applications of Computer Vision Workshops (WACVW)},
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pages={41--46},
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year={2019},
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organization={IEEE}
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}
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```
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## Introduction
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We utilize state-of-the-art object deteciton and tracking algorithm in surveillance videos. Our best object detection model basically uses Faster RCNN with a backbone of Resnet-101 with dilated CNN and FPN. The tracking algo (Deep SORT) uses ROI features from the object detection model.
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<div align="center">
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<div style="">
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<img src="images/Person_vis_video.gif" height="300px" />
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<img src="images/Vehicle_vis_video.gif" height="300px" />
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</div>
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</div>
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## Dependencies
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The code is originally written for Tensorflow v1.10 with Python 2.7 but it works on v1.13.1, too. Note that I didn't change the code for v1.13.1 instead I just disable Tensorflow warnings and logging.
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Other dependencies: numpy; scipy; sklearn; cv2
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## Code Overview
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## Inferencing
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1. First download some test videos and the v3 model:
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```
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$ wget https://aladdin-eax.inf.cs.cmu.edu/shares/diva_obj_detect_models/models/v1-val_testvideos.tgz
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$ tar -zxvf v1-val_testvideos.tgz
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$ ls v1-val_testvideos > v1-val_testvideos.lst
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$ wget https://aladdin-eax.inf.cs.cmu.edu/shares/diva_obj_detect_models/models/obj_v3_model.tgz
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$ tar -zxvf obj_v3_model.tgz
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```
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2. Run object detection on the test videos
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```
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$ python obj_detect.py --model_path obj_v3_model --version 3 --video_dir v1-val_testvideos --video_lst_file v1-val_testvideos.lst --out_dir test_json_out --frame_gap 1 --visualize --vis_path test_vis_out --get_box_feat --box_feat_path test_box_feat_out
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```
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The object detection output for each frame will be in `test_json_out/` and in COCO format. The visualization frames will be in `test_vis_out/`. The ROI features will be in `test_box_feat_out/`. Remove `--visualize --vis_path test_vis_out` and `--get_box_feat --box_feat_path test_box_feat_out` if you only want the json files.
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3. Run object detection & tracking on the test videos
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```
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$ python obj_detect_tracking.py --model_path obj_v3_model --version 3 --video_dir v1-val_testvideos --video_lst_file v1-val_testvideos.lst --out_dir test_json_out --frame_gap 1 --get_tracking --tracking_dir test_track_out
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```
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The tracking results will be in `test_track_out/` and in MOTChallenge format. To visualize the tracking results:
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```
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$ ls $PWD/v1-val_testvideos/* > v1-val_testvideos.abs.lst
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$ python get_frames_resize.py v1-val_testvideos.abs.lst v1-val_testvideos_frames/ --use_2level
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$ cd test_track_out/VIRAT_S_000205_05_001092_001124.mp4
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$ ls Person > Person.lst; ls Vehicle > Vehicle.lst
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$ python ../../track_to_json.py Vehicle Vehicle.lst Vehicle Vehicle_json
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$ python ../../track_to_json.py Person Person.lst Person Person_json
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$ python ../../vis_json.py Person.lst ../../v1-val_testvideos_frames/ Person_json/ Person_vis
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$ python ../../vis_json.py Vehicle.lst ../../v1-val_testvideos_frames/ Vehicle_json/ Vehicle_vis
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$ ffmpeg -framerate 30 -i Vehicle_vis/VIRAT_S_000205_05_001092_001124/VIRAT_S_000205_05_001092_001124_F_%08d.jpg Vehicle_vis_video.mp4
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$ ffmpeg -framerate 30 -i Person_vis/VIRAT_S_000205_05_001092_001124/VIRAT_S_000205_05_001092_001124_F_%08d.jpg Person_vis_video.mp4
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```
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Now you have the tracking visualization videos for both "Person" and "Vehicle" class.
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## Models
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These are the models you can use for inferencing. The original ActEv annotations can be downloaded from [here](https://next.cs.cmu.edu/data/actev-v1-drop4-yaml.tgz). I will add instruction for training and testing if requested.
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<table>
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<tr>
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<td colspan="6">
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<a href="https://aladdin-eax.inf.cs.cmu.edu/shares/diva_obj_detect_models/models/obj_v2_model.tgz">Object v2</a>
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: Trained on v1-train</td>
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</tr>
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<tr>
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<td>Eval on v1-val</td>
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<td>Person</td>
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<td>Prop</td>
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<td>Push_Pulled_Object</td>
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<td>Vehicle</td>
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<td>Mean</td>
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</tr>
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<tr>
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<td>AP</td>
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<td>0.831</td>
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<td>0.405</td>
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<td>0.682</td>
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<td>0.982</td>
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<td>0.725</td>
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</tr>
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<tr>
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<td>AR</td>
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<td>0.906</td>
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<td>0.915</td>
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<td>0.899</td>
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<td>0.983</td>
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<td>0.926</td>
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</tr>
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</table>
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<table>
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<tr>
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<td colspan="6">
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<a href="https://aladdin-eax.inf.cs.cmu.edu/shares/diva_obj_detect_models/models/obj_v3_model.tgz">Object v3</a>
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: Trained on v1-train</td>
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</tr>
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<tr>
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<td>Eval on v1-val</td>
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<td>Person</td>
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<td>Prop</td>
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<td>Push_Pulled_Object</td>
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<td>Vehicle</td>
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<td>Mean</td>
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</tr>
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<tr>
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<td>AP</td>
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<td>0.836</td>
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<td>0.448</td>
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<td>0.702</td>
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<td>0.984</td>
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<td>0.742</td>
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</tr>
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<tr>
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<td>AR</td>
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<td>0.911</td>
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<td>0.910</td>
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<td>0.895</td>
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<td>0.985</td>
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<td>0.925</td>
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</tr>
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</table>
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## Other things I have tried
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These are my experiences with working on this (surveillance dataset)[https://actev.nist.gov/]:
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1. FPN provides significant improvement over non-FPN backbone;
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2. Dilated CNN in backbone also helps;
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3. Cascade RCNN doesn't help (IOU=0.5). I'm using IOU=0.5 in my evaluation since the original annotations are not "tight" bounding boxes.
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4. Decoupled RCNN slightly improves AP (Person: 0.836 -> 0.837) but takes 7x more time.

‎application_util/__init__.py

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# vim: expandtab:ts=4:sw=4

‎application_util/__init__.pyc

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