Object Detection in Video using Thresholding Technique

Problem Definition

Problem Description: The increasing use of videos in various applications has created a need for efficient techniques to detect objects in videos. Traditional object detection methods may not be suitable for videos due to the continuous stream of frames. This project focuses on designing an approach using thresholding methodology to detect objects in videos. The problem to be addressed is how to effectively detect objects in a video by setting a threshold value and processing each frame to identify and locate objects accurately. Traditional object detection methods may not be robust for video processing, and hence, a new approach is required to address this challenge.

By implementing this project, researchers and engineers can explore new possibilities for object detection in videos with improved accuracy and efficiency.

Proposed Work

The proposed M-tech level project titled "An approach designing for object detection in video using thresholding methodology" focuses on detecting objects in a video by setting a threshold value. The project falls under the category of Video Processing Based Projects and is implemented using MATLAB software. This project can be undertaken by both electronics engineering and computer science engineering students. With the increasing focus on video processing research such as watermarking, data hiding, and object detection in videos, this project stands out as a live application where objects can be detected in real-time videos. The project treats each frame of the video as an individual image, processing them to detect objects by comparing pixel values with the set threshold.

By utilizing a threshold value, the technique efficiently identifies objects in the video based on their color properties. This project presents an innovative and effective method for object detection in videos, showcasing the potential of thresholding methodology in video processing.

Application Area for Industry

This project can be used in various industrial sectors such as surveillance, automotive, manufacturing, and healthcare. In the surveillance sector, this project's proposed solutions can help in detecting and tracking objects in real-time videos, improving overall security measures. In the automotive industry, the project can be used for detecting obstacles on the road or monitoring driver behavior. In manufacturing, object detection in videos can enhance quality control processes by identifying defects or anomalies in products. In the healthcare sector, the project can be applied for monitoring patient movements or detecting abnormalities in medical images and videos.

Specific challenges that industries face include the need for real-time object detection, accurate identification of objects, and efficient processing of video data. By implementing the proposed approach using thresholding methodology, industries can overcome these challenges and benefit from improved accuracy and efficiency in object detection. The project's focus on setting a threshold value and processing each frame individually allows for precise identification and location of objects in videos, making it a valuable tool for industries where object detection plays a critical role in operations. Overall, this project presents a promising solution for enhancing object detection capabilities in various industrial domains with its innovative approach and potential for improving video processing efficiency.

Application Area for Academics

The proposed project on object detection in videos using thresholding methodology holds significant relevance for research by MTech and PHD students in the fields of electronics engineering and computer science engineering. This project offers a unique approach to detecting objects in videos by implementing thresholding techniques, which may not be covered in traditional object detection methods. MTech and PHD students can utilize this project for their research by exploring innovative methods for accurate and efficient object detection in videos. The code and literature of this project can serve as a valuable resource for students working on dissertations, theses, or research papers related to video processing and object detection. By working on this project, researchers can gain insights into new possibilities for improving object detection in videos, leading to advancements in video processing technologies.

Furthermore, this project opens up avenues for simulation studies, data analysis, and experimentation in the domain of video processing, providing a solid foundation for innovative research methods. The future scope of this project includes further refinement of the thresholding methodology for object detection in videos, exploring applications in real-world scenarios, and integrating advanced technologies for enhanced object recognition capabilities. In conclusion, this project offers a valuable opportunity for MTech and PHD students to conduct research in video processing and object detection, paving the way for groundbreaking advancements in the field.

Keywords

object detection, video processing, thresholding methodology, MATLAB, real-time videos, object identification, pixel values, color properties, video surveillance, image segmentation, object tracking, image retrieval, new technology, advanced algorithms, video analysis, computer vision, machine learning