MotionPathsExtraction

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MotionPathsExtraction

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Approach to extract motion paths (trajectories) of vehicles and pedestrians from videos (Multiple object detection & tracking)

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# MotionPathsExtraction

Multi object and classes detection and tracking pipeline to extract motion paths of objects like vehicles and pedestrians from videos.

#### Please note:
This approach does not implement it´s own detection and tracking algorithms but makes use of the following algorithms:  
* Facebook´s [Detectron](https://github.com/facebookresearch/Detectron) Mask/Faster R-CNN implementations:
  * e2e_mask_rcnn_R-101-FPN_2x (coco_2014_train and coco_2014_valminusminival)
  * e2e_faster_rcnn_R-101-FPN_2x (coco_2014_train and coco_2014_valminusminival)
  * others from the [Detectron Model Zoo](https://github.com/facebookresearch/Detectron/blob/master/MODEL_ZOO.md) can be used per config file and detectrons auto-download functionality
* [SORT](https://github.com/abewley/sort) - A simple online and realtime tracking algorithm for 2D multiple object tracking in video sequences
* [Deep SORT](https://github.com/nwojke/deep_sort) - Simple Online Realtime Tracking with a Deep Association Metric

I recommend to have a look at:

-  [Simple Online and Realtime Tracking](http://arxiv.org/abs/1602.00763)
-  [Simple Online and Realtime Tracking with a Deep Association Metric](https://arxiv.org/abs/1703.07402)
-  [Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks](https://arxiv.org/pdf/1506.01497)

We also make use of [ChainerCV](https://github.com/chainer/chainercv) and its detector implementations of FasterR-CNN, SSD and YOLO:
* faster_rcnn_vgg16_voc0712_trained
* faster_rcnn_fpn_resnet50_coco_trained
* faster_rcnn_fpn_resnet101_coco_trained
* ssd300_voc0712_converted
* ssd512_voc0712_converted
* yolo_v2_tiny_voc0712_converted
* yolo_v2_voc0712_converted
* yolo_v3_voc0712_converted

![Poster](https://raw.githubusercontent.com/mavoll/MotionPathsExtraction/master/poster/poster_A0.jpg)

## Prerequisites: ###

- Ubuntu 16.04 or 18.04
- CUDA-ready NVIDIA GPU ([check](https://www.geforce.com/hardware/technology/cuda/supported-gpus))
- CUDA >= 9.0 (< 10.1)
- cuDNN >= 7.1.3
- Python 2.7 or 3.6 (not 3.7)
- OpenCV 3.4 (not 4)
- Caffe2 >= 0.7.0 or PyTorch >= 1.0 (to do inference and training with detector Detectron)
- PyYAML == 3.12
- COCO API (see [here](https://github.com/mavoll/MotionPathsExtraction/blob/master/Detectron/INSTALL.md#coco) and [here](https://github.com/cocodataset/cocoapi))
- TensorFlow >= 1.4.0 (person re-identification feature generation for tracker deep_sort)
- Chainer >= 4
- cupy >= 4
- chainercv >= 0.10 (to do inference and training with detector chainercv and its implementations of FasterR-CNN, SSD and YOLO)
- (Anaconda 2018.12)

## Get your environment ready: ###

Tested with:
- NVIDIA GeForce GTX 1080ti 11 GB (Ubuntu 16.04, python 2.7, CUDA 9.0, cuDNN 7.1.3, Driver 384.111, TensorFlow 1.8.1, Caffe2 0.7.0 , OpenCV 3.4, Chainer 5.3.0, Cupy 5.1.0, , ChainerCV 0.10)
- Dual-GPU: 2 x NVIDIA GeForce GTX 1080ti 11 GB (Ubuntu 16.04, python 2.7, CUDA 9.0, cuDNN 7.1.3, Driver 384.111, TensorFlow 1.8.1, Caffe2 0.7.0 , OpenCV 3.4, Chainer 5.3.0, Cupy 5.1.0, , ChainerCV 0.10)
- NVIDIA GeForce RTX 2070 8 GB (Ubuntu 18.04, python 3.6, CUDA 10.0, cuDNN 7.3.1, Driver 418.43, TensorFlow 1.11.0, PyTorch (Caffe2) 1.0.1, OpenCV 3.4, Chainer 5.3.0, Cupy 5.1.0 , ChainerCV 0.10)

I installed Anaconda ([from here](https://www.anaconda.com/distribution/#linux)) to create an environment and to install components.
For example:

### Install cuda and cudnn: ###
```
sudo rm /etc/apt/sources.list.d/cuda*
```
```
sudo apt remove nvidia-cuda-toolkit
```
```
sudo apt remove nvidia-*
```
```
sudo apt update
```
```
sudo add-apt-repository ppa:graphics-drivers/ppa
```
```
sudo apt-key adv --fetch-keys  http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1804/x86_64/7fa2af80.pub
```
```
sudo bash -c 'echo "deb http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1804/x86_64 /" > /etc/apt/sources.list.d/cuda.list'
```
```
sudo bash -c 'echo "deb http://developer.download.nvidia.com/compute/machine-learning/repos/ubuntu1804/x86_64 /" > /etc/apt/sources.list.d/cuda_learn.list'
```
```
sudo apt update
```
```
sudo apt install cuda-drivers=410.104-1
```
```
sudo apt install cuda-runtime-10-0
```
```
sudo apt install cuda-10-0
```
```
sudo apt install libcudnn7
```
```
nvidia-smi
```
```
nvcc --version
```
```
sudo apt install cuda-toolkit-10-0
```
```
export PATH=/usr/local/cuda-10.0/bin${PATH:+:${PATH}}
```
```
export LD_LIBRARY_PATH=/usr/local/cuda-10.0/lib64${LD_LIBRARY_PATH:+:${LD_LIBRARY_PATH}}
```

Add to `~/.profile':

```
PATH="$HOME/bin:$HOME/.local/bin:$PATH"

# set PATH for cuda 10.0 installation
if [ -d "/usr/local/cuda-10.0/bin/" ]; then
    export PATH=/usr/local/cuda-10.0/bin${PATH:+:${PATH}}
    export LD_LIBRARY_PATH=/usr/local/cuda-10.0/lib64${LD_LIBRARY_PATH:+:${LD_LIBRARY_PATH}}
fi
```
```
sudo reboot
```

### Install Anaconda: ###

```
wget https://repo.anaconda.com/archive/Anaconda3-2018.12-Linux-x86_64.sh
```
```
bash Anaconda3-2018.12-Linux-x86_64.sh
```
```
source ~/.bashrc
```
```
conda info
```
```
conda update conda
```
```
conda update anaconda
```
```
conda update anaconda
```

### Create environment: ###

```
conda create --name envName python=3.6
```
```
conda activate envName
```
```
conda install ipykernel
```
```
python -m ipykernel install --user --name envName
```

### Install packages: ###

```
conda install pip  
```
```
pip install imutils
```
```
conda install numpy pyyaml matplotlib setuptools scipy protobuf future mkl mkl-include libtool
```
```
conda install -c mingfeima mkldnn
```
```
conda install nnpack
```
```
conda install -c conda-forge ffmpeg
```
```
pip install opencv-contrib-python==3.4.4.19
```
```
pip install https://download.pytorch.org/whl/cu100/torch-1.0.1.post2-cp36-cp36m-linux_x86_64.whl
```
```
pip install tensorflow-gpu
```
```
conda install scikit-learn
```
```
conda install -c hcc pycocotools
```
```
conda install -c anaconda chainer
```
```
pip install cupy
```
```
pip install chainercv
```
```
conda install -c conda-forge filterpy
```

### Test Installations within Python: ###

```
import chainer
import cv2
import tensorflow as tf
import torch
import caffe2
chainer.__version__
cv2.__version__
tf.__version__
torch.__version__
caffe2.__version__
```

If you used to install old caffe2, the old caffe2 libcaffe2.so, libcaffe2_detectron_ops_gpu.so, libcaffe2_gpu.so, libcaffe2_module_test_dynamic.so, libcaffe2_observers.so is in /usr/local/lib, but now new installed caffe2 they all in pytorch/build/lib. Make sure to delete all in /usr/local/lib.

## Install: ##

```
cd ~
```
```
git clone https://github.com/mavoll/MotionPathsExtraction.git
```
```
cd MotionPathsExtraction
```
```
git clone https://github.com/facebookresearch/Detectron.git
```
```
cd Detectron
```
```
make
```
```
cd ..
```
```
git clone https://github.com/nwojke/deep_sort.git
```
```
git clone https://github.com/abewley/sort.git
```

### Download Resources: ###

You need to download some resources in order to make it work.

-  Pre-generated CNN checkpoint file (mars-small128.pb) from [here](https://drive.google.com/open?id=18fKzfqnqhqW3s9zwsCbnVJ5XF2JFeqMp) provided from [Deep SORT](https://github.com/nwojke/deep_sort). 
-  End-to-End Faster & Mask R-CNN Baselines from Detectrons Model Zoo ([Faster-RCNN: R-101-FPN](https://dl.fbaipublicfiles.com/detectron/35857952/12_2017_baselines/e2e_faster_rcnn_R-101-FPN_2x.yaml.01_39_49.JPwJDh92/output/train/coco_2014_train%3Acoco_2014_valminusminival/generalized_rcnn/model_final.pkl) and [Mask-RCNN: R-101-FPN](https://dl.fbaipublicfiles.com/detectron/35861858/12_2017_baselines/e2e_mask_rcnn_R-101-FPN_2x.yaml.02_32_51.SgT4y1cO/output/train/coco_2014_train%3Acoco_2014_valminusminival/generalized_rcnn/model_final.pkl) used here.

You can use other models from the model zoo by downloading them and set cfg and wts within config.ini accordingly.

The easiest way is to use the auto download functionalities provided by Detectron and Chainer and additionally download manually CNN checkpoint file which is used for feature generation (Tensorflow) by the Tracker Deep SORT.

Just make sure that you set following parameter in config.ini:

```
[Detectron]
download_cache = SOMEPATH/detectron-download-cache

[deep_sort_features]
model = SOMEPATH/networks/mars-small128.pb
```
`ChainerCV will automatically download to ~/.chainer`.

```
python detect_and_track.py
```

### Parameter: ###
Use the GUI or the config.ini to change and test detector or tracker related parameter.

Two parameter to mention:

`per_process_gpu_mem_fraction = 0.1` is set here depending on used GPU. It is necessary to make sure that the GPU can load both, the detector and the tracker model, at the same time to be able to initialize detector and tracker at the beginning of the pipeline and not for each frame or detector and tracker sequentially.

`imutils_queue_size = 128` sets the input buffer queue size. 0 is infinite. Imagine, that this queue will got filled fast if input frame rate is higher than processing framerate. Thanks to [imutils](https://github.com/jrosebr1/imutils) for providing this out-of-the-box.

[This](https://github.com/facebookresearch/Detectron) page provides detailed information about Facebooks tracker Detectron and its Model Zoo.
Here is a end-to-end trained Mask R-CNN model with a ResNet-101-FPN backbone from [here](https://dl.fbaipublicfiles.com/detectron/35861858/12_2017_baselines/e2e_mask_rcnn_R-101-FPN_2x.yaml.02_32_51.SgT4y1cO/output/train/coco_2014_train%3Acoco_2014_valminusminival/generalized_rcnn/model_final.pkl) used.
All models based on the [COCO dataset](http://cocodataset.org/#home).

**Detectron Troubleshooting**

[INSTALL.md](https://github.com/mavoll/MotionPathsExtraction/edit/master/Detectron/INSTALL.md)

**deep_sort Troubleshooting**

[README.md](https://github.com/mavoll/MotionPathsExtraction/blob/master/deep_sort/README.md)

## Usage: ##

-  Change options, save and load config files
-  Choose detector (and options) to use
-  Choose tracker (and options) to use 
-  Choose input source and run

-  See FPS (also displayed on screen) and other timer infos on logging window

Mask R-CNN in combination with deep_sort (here 1 FPS on GeForce RTX2070 8GB):

Faster R-CNN in combination with deep_sort (here 5 FPS on GeForce RTX2070 8GB):

SSD and SORT (here 15 FPS on GeForce RTX2070 8GB):

You have to consider, that if you want to have a Faster R-CNN -powerful detection engine running almost in real-time, than it might be possible or good enough with limited FPS. But if you also need to get good tracking results with less identity switches, than you need to feed your tracker with >20 FPS. So a real-time detection/tracking pipeline (with Detections of Pedestrians on Faster R-CNN level) is hard to archive (especially with mobile Hardware like NVIDIA Jetson TX2 but also not possible with cards like GTX 1080 Ti 11GB or RTX 2070 8GB). SSD with SORT runs on NVIDIA TX2 almost fluently. See later results.

If you have use-cases in mind requiring streaming images from multiple computers/Pis to an processing server, than check [pyimagesearch’s post](https://www.pyimagesearch.com/2019/04/15/live-video-streaming-over-network-with-opencv-and-imagezmq) and [imagezmq](https://github.com/jeffbass/imagezmq).

## Multi GPU batch usage (dataset-level): ###

Only on dataset level, because tracker and detector models fit into one GPU at the same time so using multiple processes (Pythons multiprocessing) and instances, two per GPU, to batch process bigger input data should be ok.

Bulk processing (fasterrcnn and deep_sort) 4 instances (2 processes per GPU) running recursively on different forders and subfolders:

Parameters influencing the runtime most:

`detectron_score_thresh = 0.6` higher is faster but also results in less trajectories. Determines the min. value of the score/confidence of an detection to consider them for tracking.

`deep_sort_min_confidence = 128` same as for detectron_score_thresh but for tracks.

How to start:

Evaluate the best combination of number instances (parallel processes) per GPU in terms of GPU Memory, RAM, and CPU usage (with 2 GeForce GTX 1080 Ti 11 GB, 32 GB RAM and 8 i7 cores; 2 instances per GPU is good and delivers an parallelization factor of 4).

Create/Modify `config_gpu_x.ini` files for each GPU.

Modify `bulk_config.ini` and determine the input folders (each instance will recursively walk through its corresponding folder and process every file matching the file type) for each instance.

`python bulk_processing.py`

## Further Usage based on the tracking results: ##

### Count intersections

Use the [CountingTool](https://github.com/mavoll/TrafficCountingTool) to draw lines and count intersections.

### Mapping

Use the [Mapping](https://github.com/mavoll/SimpleTPSMapping) to map pixel coordinates to geo-coordinates.

### Animate and analyze tracks

#### Import tracking results to PostGIS

Install QGIS:
```
sudo sh -c 'echo "deb http://qgis.org/debian bionic main " >> /etc/apt/sources.list'
sudo sh -c 'echo "deb-src http://qgis.org/debian bionic main " >> /etc/apt/sources.list'
sudo apt update
sudo apt install qgis python3-qgis qgis-plugin-grass
```

Install PostGIS:
```
sudo sh -c 'echo "deb http://apt.postgresql.org/pub/repos/apt bionic-pgdg main" >> /etc/apt/sources.list'
wget --quiet -O - http://apt.postgresql.org/pub/repos/apt/ACCC4CF8.asc | sudo apt-key add -
sudo apt update
sudo apt install postgresql-11-postgis-2.5
sudo apt install postgis

sudo -u postgres psql
CREATE EXTENSION adminpack;
CREATE DATABASE gisdb;
\connect gisdb;
CREATE SCHEMA postgis;
ALTER DATABASE gisdb SET search_path=public, postgis, contrib;
\connect gisdb;
CREATE EXTENSION postgis SCHEMA postgis;

CREATE EXTENSION postgis_sfcgal SCHEMA postgis;

\password postgres
```

For more PostGIS configuration see [here](http://trac.osgeo.org/postgis/wiki/UsersWikiPostGIS24UbuntuPGSQL10Apt).

Create tracks table (geometry Point):
```
CREATE TABLE postgis.tracks_points_per_sec
(
  slice text NOT NULL,
  cam text NOT NULL,
  day text NOT NULL,
  part integer NOT NULL,
  subpart integer NOT NULL,
  track_id integer NOT NULL,
  time timestamp NOT NULL,
  track_class text NOT NULL,
  geom geometry(Point, 5555) NOT NULL,
  PRIMARY KEY (slice, cam, day, part, subpart, track_id, time)
);

```
Import to postgis.tracks_points_per_sec from georeferenced tracks in csv file:
```
python scripts/insert_csv_tracks_into_postgis_point_date_sec.py -r 25 -y 1521027720 -e 'gisdb' -u 'postgres' -w 'postgres' -f 'scripts/geo_ref_tracks.csv' -t 'tracks_points_per_sec' -s 'Testdatensatz' -d 'Testdatensatz' -p 1 -b 1 -i 'localhost' -x 5432
```

Create tracks table (geometry LineStringM):
```
CREATE TABLE postgis.tracks_linestrings_per_sec
(
  slice text NOT NULL,
  cam text NOT NULL,
  day text NOT NULL,
  part integer NOT NULL,
  subpart integer NOT NULL,
  starttime timestamp NOT NULL,
  endtime timestamp NOT NULL,
  track_time_range tsrange NOT NULL,
  frame_rate text NOT NULL,
  track_class text NOT NULL,
  track_id integer NOT NULL,
  geom geometry(LineStringM, 5555),
  PRIMARY KEY (slice, cam, day, part, subpart, track_id)
);

```
Import to postgis.tracks_linestringm_per_sec from georeferenced tracks in csv file:
```
python scripts/insert_csv_tracks_into_postgis_linestringm_sec.py -r 25 -y 1521027720 -e 'gisdb' -u 'postgres' -w 'postgres' -f 'scripts/geo_ref_tracks.csv' -t 'tracks_linestrings_per_sec' -s 'Testdatensatz' -d 'Testdatensatz' -p 1 -b 1 -i 'localhost' -x 5432
```

#### Using QGIS and its TimeManager

Open QGIS and create new PostGIS connection (name: tracks, host: localhost, port: 5432, database: gisdb, user: postgres, password: postgres).
Use QGIS DB Manager to check your tables tracks_linestrings_per_sec and tracks_points_per_sec.
Install QGIS Plugin 'TimeManager' within QGIS GUI.
Turn off the TimeManager.
Add Layer OpenStreetMap from category xyz tiles and zoom to Domplatz.
Add Layer tracks_points_per_sec from category PostGIS -> tracks -> postgis

In tracks_points_per_sec Layer Properties in category 'Symbology' choose 'Categorized' select 'track_class' as column and classify. From the result list deselect all except 1 for person and 3 for car (to see only persons and cars and to give them different colors for the animation).

Open TimeManager settings and add layer (layer: tracks_points_per_sec, start time: time, offset: 1, accumulate features: whateveryoulike). You can also use the tracks_linestrings_per_sec.
Select for 'show frame for' 1000 millisecs (FPS 25) and as 'time frame size' 1 sec.

Turn on TimeManager and play.

#### Short map animation examples (created using QGIS TimeManager):

[points animation](https://drive.google.com/file/d/1RaxYcd6amxBYuJ8-cyD-YrJONbC3HBFw/view?usp=sharing)

[point cluster animation](https://drive.google.com/file/d/1BHM0dLm705j1DTThtmIYAvkwDjk1B2qL/view?usp=sharing)

[heatmap animation](https://drive.google.com/file/d/1y_93ddwSI3TcItxy8eM7_P-pP9gDOply/view?usp=sharing)

### Tracks to the SparkPipeline

#### Import
[SparkPipeline](https://github.com/mavoll/SparkPipeline)

#### Using Apache Zeppelin and Spark to analyze and visualize tracks

## Further development and research opportunities

* Use-case specific circumstances:
  * Due to tracking-related identity switches this approach produces (depending on the crowded scene) shorter and fractional trajectories compared for example with approaches using mobile GPS devices to produce long and unique tracks. 
  * We are using multiple cams and perspectives to observe the whole plaza. Those perspectives only overlap at the edges. 
* Corresponding research questions:
  * Besides improvement within the field of multiple object detection and tracking producing less and less identity switches, is it possible to develop a post-processing process to re-connect corresponding trajectories or to generalize those trajectories in order to use well known analysis techniques requiring longer trajectories.
  * How to connect the trajectories from diffenet perspectives if objects moving from one perspective/cam to another.
  * Calculate frequency and dwell time for recorded times and areas. Develop simple linear regression model to analyse correlation between frequency and dwell time. Predict/extrapolate frequency and dwelltime over time using autoregressive models (for example ARIMA). Develop multidimensional linear regression model and analyse frequency and dwelltime depending on other variables like for example social media activity or detected events.    
  * Find a metric (besides multiple object tracking challenges like for example the [MOT challange](https://arxiv.org/pdf/1603.00831.pdf)) to evaluate the whole process of trajectory extraction focusing on the needs of social scientists and there requirements on the data to be able to produce significant experiments. 
  * State-of-the-art path manipulation and analysis keeping our specific circumstances in mind.
  * Using extracted real motion paths of vehicles and pedestrians to train, test and evaluate Agent-based simulation models or to configurate models with help from real countings/statistics (for example the measured usual and real frequency and dwell time of Pedestrians and cars at specific time and place)?

## Authors

* **Marc-André Vollstedt** - marc.vollstedt@gmail.com

## Acknowledgments

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