Dimentional based object detection algorithm (DBOD)

This is a C++ version of DBOD algorithm written in Python

The current repository is an implementation of detection method for low-performance Linux single-board computers. The method is used for detection of pedestrians, cyclists and vehicles in city environment. The method is based on analysis of geometrical object features in a foreground mask. The foreground mask is obtained using background subtraction algorithm. Classification is performed using logistic regression classifier. Implementation of the method is based on the publication “Fast Object Detection Using Dimensional Based Features for Public Street Environments”.

Prerequisites

The method can be used only when following conditions are satisfied:

1. Known intrinsic and extrinsic (angle about X axis and height of installation) camera parameters.
2. The camera is mounted on a static object.
3. The trained classifier for a particular usage scenario. The training uses 3D object models and camera parameters on input.

Installation

Build and install the required libraries:

• OpenCV
• yaml-cpp

Build the DBOD

• Create build directory:

mkdir build

• Generate cmkae files

cmake build/

• Start building

cmake --build build/ -- -j4