REAL-TIME OBJECT DETECTION IN VIDEOS USING DEEP LEARNING MODELS

Abstract

Video object detection plays a pivotal role in various applications, from surveillance to autonomous vehicles. This research addresses the need for real-time object detection in videos using advanced deep learning models. The current landscape of object detection techniques often struggles to maintain efficiency in processing video streams, leading to delays and resource-intensive computations. This study aims to bridge this gap by proposing a novel methodology for real-time object detection in videos. With the surge in video data across domains, the demand for swift and accurate object detection in real-time has become imperative. Existing methods face challenges in balancing speed and precision, prompting the exploration of more robust solutions. This research endeavors to enhance the efficiency of video object detection, offering a timely and accurate approach to address contemporary demands. The primary challenge lies in achieving real-time object detection without compromising accuracy. Traditional methods often compromise speed for precision, leading to inadequate performance in dynamic video environments. This study seeks to overcome this dilemma by introducing a methodology that optimizes both speed and accuracy, catering to the real-time constraints of video processing. Despite the advancements in object detection, a notable research gap exists in the domain of real-time video object detection. Existing models exhibit limitations in adapting to the dynamic nature of video streams, necessitating the development of novel methodologies. This research aims to fill this void by proposing an innovative approach that addresses the specific challenges posed by real-time video data. The proposed methodology integrates state-of-the-art deep learning models, optimizing them for real-time video object detection. Leveraging advanced architectures and streamlining the inference process, the model aims to provide accurate detections at unparalleled speeds. Additionally, a novel data augmentation technique is introduced to enhance the model’s adaptability to dynamic video scenarios. Preliminary results demonstrate the effectiveness of the proposed methodology, showcasing a significant improvement in both real-time processing speed and object detection accuracy. The model exhibits promising performance across diverse video datasets, highlighting its potential to outperform existing methods in real-world applications.