Counting Pedestrians Using TensorFlow's Object Detection

I am new to machine learning field and based on what I have seen on youtube and read on internet I conjectured that it might be possible to count pedestrians in a video using tensorflow's object detection API.

Consequently, I did some research on tensorflow and read documentation about how to install tensorflow and then finally downloaded tensorflow and installed it. Using the sample files provided on github I adapted the code related to object_detection notebook provided here ->https://github.com/tensorflow/models/tree/master/research/object_detection.

I executed the adapted code on the videos that I collected while making changes to visualization_utils.py script so as to report number of objects that cross a defined region of interest on the screen. That is I collected bounding boxes dimensions (left,right,top, bottom) of person class and counted all the detection's that crossed the defined region of interest (imagine a set of two virtual vertical lines on video frame with left and right pixel value and then comparing detected bounding box's left & right values with predefined values). However, when I use this procedure I am missing on lot of pedestrians even though they are detected by the program. That is the program correctly classifies them as persons but sometimes they don't meet the criteria that I defined for counting and as such they are not counted. I want to know if there is a better way of counting unique pedestrians using the code rather than using the simplistic method that I am trying to develop. Is the approach that I am using the right one ? Could there be other better approaches ? Would appreciate any kind of help.

Please go easy on me as I am not a machine learning expert and just a novice.

2 Answers
2

You are using a pretrained model which is trained to identify people in general. I think you're saying that some people are pedestrians whereas some other people are not pedestrians, for example, someone standing waiting at the light is a pedestrian, but someone standing in their garden behind the street is not a pedestrian.

If I'm right, then you've reached the limitations of what you'll get with this model and you will probably have to train a model yourself to do what you want.

Since you're new to ML building your own dataset and training your own model probably sounds like a tall order, there's a learning curve to be sure. So I'll suggest the easiest way forward. That is, use the object detection model to identify people, then train a new binary classification model (about the easiest model to train) to identify if a particular person is a pedestrian or not (you will create a dataset of images and 1/0 values to identify them as pedestrian or not). I suggest this because a boolean classification model is about as easy a model as you can get and there are dozens of tutorials you can follow. Here's a good one:

https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/3_NeuralNetworks/neural_network.ipynb

A few things to note when doing this:

My last bullet-point illustrates a problem with the idea as it's I've proposed it. The best solution would be to alter the object detection network to ouput both a bounding box per-person, and also a pedestrian/non pedestrian classification with it; or to only train the model to identify pedestrians, specifically, in the first place. I mention this as more optimal, but I consider it a more advanced task than my first suggestion, and a more complex dataset to manage. It's probably not the first thing you want to tackle as you learn your way around ML.

The pipeline of the tracker that is developed with OpenCV and TensorFlow!

Pedestrian Counting and Vehicle Counting Sample Projects are available in this repo! It provides a TensorFlow Object Counting API is an open source framework built on top of TensorFlow that makes it easy to develop object counting systems!

You can watch the demo video of Pedestrian Counting and Vehicle Counting projects!

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