Object Detection using Image Processing
This work addresses border security and surveillance for military applications, but it is incremental as it applies an existing method to a specific domain.
The paper tackled object detection for UAVs to prevent collisions by developing an OpenCV-Python code using the Haar Cascade algorithm, resulting in reduced processing time as verified through testing on video and image databases.
An Unmanned Ariel vehicle (UAV) has greater importance in the army for border security. The main objective of this article is to develop an OpenCV-Python code using Haar Cascade algorithm for object and face detection. Currently, UAVs are used for detecting and attacking the infiltrated ground targets. The main drawback for this type of UAVs is that sometimes the object are not properly detected, which thereby causes the object to hit the UAV. This project aims to avoid such unwanted collisions and damages of UAV. UAV is also used for surveillance that uses Voila-jones algorithm to detect and track humans. This algorithm uses cascade object detector function and vision. train function to train the algorithm. The main advantage of this code is the reduced processing time. The Python code was tested with the help of available database of video and image, the output was verified.