A combination between people tracking and age and gender detection
- Combining people tracking with age and gender detection is a good idea for many and many applications in real life scenarios such as caffe store management to gather the information of customers for further analysis, or in/out people control for security purposes in buildings ...
- This is just a small step of putting the state-of-the-art image processing techniques together.
- Firstly, faces are detected in the frame using the famous caffe model res10_300x300_ssd_iter_140000.caffemodel.
- Secondly, age and gender of every person is predicted also using caffe models age_net and gender_net.
- Each person is then assigned an ID and tracked over time, even when they are out of the frame for not so long (5 seconds), whenever they come back in the frame, their ID will remain the same.
- A picture of the person is then saved with the information of him/her.
- Python 3.5
- cv2
- imutils
- Because the model files is bigger than 25MB so I can't put it here, you need to download it -->here <-- and place them in the folder age_gender_models.
- Run
python object_tracker.py(sorry for the name, it should be people_tracker but I was too lazy to change it :))
- I can get 16fps in my Core i5 desktop with the solution of 640x480.
- Basically, the available age and gender models are fairly accurate, I've just turned 25 two days ago :).
- If you want to train your own models for age and gender detection, have a look at https://github.com/dpressel/rude-carnie.
A great thank to those who have done fantastic work
- https://github.com/eveningglow/age-and-gender-classification
- https://www.pyimagesearch.com/2018/07/23/simple-object-tracking-with-opencv/
- Inform me in case you have any problem with running the code.
- Any related idea is welcome at khanhhanguyen2310@gmail.com
- Age and gender detection don't need to be performed every frame. Instead, we can detect every 5 or 10 frames to improve the speed of the program.