Movement classification

The goal of this project would be movement classification of people, in other words, walking (normal and fast) and running.

Yolov4 will be used for detection.

Yolov4 detection

Yolo: Real-Time object detection

You only look once (YOLO) is a state-of-the-art, real-time object detection system.

Currently the most advanced YOLO version is YOLOv4 which provides optimal speed and accuracy for object detection, therefore it will be used.

Modules

Before starting, usage of a virtual environment is advised via the venv module:

$ python3 -m venv envname # to create the virtual env
$ source envname/bin/activate # activate it
$ deactivate # when done

For ease of use, the yolov4 Python module was used, which is a YOLOv4 implementation in TensorFlow 2. For further documentation refer to the project wiki

To install yolov4:

Dependencies:

$ python3 -m pip install opencv-python tensorflow

Note: If TensorFlow Lite needs to be used, refer to the project wiki for further instructions.

TensorFlow YOLOv4:

$ python3 -m pip install yolov4

Download the yolov4-tiny and yolov4 weights to the weights/ directory from the project wiki weights download section.

Test yolov4 with the provided default test image. Change model config, weights based on the one used (default is yolov4-tiny).

$ python3 test.py

Test results

KACAVIS runaway_walk_1.mp4 frame 1471 was used:

YOLOv4	YOLOv4-tiny

Help

>>> from yolov4.tf import YOLOv4
>>> help(YOLOv4)

Dataset

FER's dataset: KACAVIS.`

Download the dataset:

wget -O dataset/crowd_simulation_dataset.zip  http://kacavis.zemris.fer.hr/datasets/Crowd_simulation_dataset_videos.zip

Pip freeze

To get the used module versions, in other words $ python3 -m pip freeze, take a look at:

./pip_freeze.txt

Tested on Archlinux 5.12.x-arch1-1, python version Python 3.9.5

If working on Arch change python3 to python everywhere.