Pedestrian-Detection

Pedestrian Detection using the TensorFlow Object Detection API and Nanonets. [Blog][Performance]

This repo provides complementary material to this blog post, which compares the performance of four object detectors for a pedestrian detection task. It also introduces a feature to use multiple GPUs in parallel for inference using the multiprocessing package. The count accuracy and FPS for different models (using 1,2,4 or 8 GPUs in parallel) were calculated and plotted.

Dataset

The TownCentre dataset is used for training our pedestrian detector. You can use the following commands to download the dataset. This automatically extracts the frames from the video, and creates XML files from the csv groundtruth. The image dimensions are downscaled by a factor of 2 to reduce processing overhead.

wget http://www.robots.ox.ac.uk/ActiveVision/Research/Projects/2009bbenfold_headpose/Datasets/TownCentreXVID.avi
wget http://www.robots.ox.ac.uk/ActiveVision/Research/Projects/2009bbenfold_headpose/Datasets/TownCentre-groundtruth.top
python extract_towncentre.py
python extract_GT.py

Setup

1. For TensorFlow Object Detection API

Refer to the instructions in this blog post.

2. For Nanonets

Step 1: Clone the repo

git clone https://github.com/NanoNets/object-detection-sample-python.git
cd object-detection-sample-python
sudo pip install requests

Step 2: Get your free API Key

Get your free API Key from http://app.nanonets.com/user/api_key

Step 3: Set the API key as an Environment Variable

export NANONETS_API_KEY=YOUR_API_KEY_GOES_HERE

Step 4: Create a New Model

python ./code/create-model.py

Note: An environment variable NANONETS_MODEL_ID will be created in the previous step, with your model ID.

Step 5: Upload the Training Data

Place the training data in a folder named images and annotations in annotations/json

python ./code/upload-training.py

Step 6: Train the Model

python ./code/train-model.py

Step 7: Get Model State

The model takes ~2 hours to train. You will get an email once the model is trained. In the meanwhile you check the state of the model

python ./code/model-state.py

Step 8: Make Predictions

Create a folder named 'test_images' inside the 'nanonets' folder. Place the input images in this folder, and then run this command.

python ./code/prediction.py

Results

FPS vs GPUs

For more stats, refer to the blog post. The performance of each model (on the test set) was compiled into a video, which you can see here.

In light of GDPR and feeble accountability of Deep Learning, it is imperative that we ponder about the legality and ethical issues concerning automation of surveillance. This blog/code is for educational purposes only, and it used a publicly available dataset. It is your responsibility to make sure that your automated system complies with the law in your region.