RECENTLY EMERGING TRENDS IN EMBEDDED COMPUTER VISION FOR OBJECT DETECTION, RECOGNITION, AND TRACKING

ABSTRACT

We explore recent advancements in embedded computer vision, particularly in object detection, recognition, and tracking. This study was motivated by the need to comprehend and optimize embedded vision technology; it meticulously examines various artificial intelligence methods and algorithms tailored for this field. By scrutinizing their strengths and weaknesses, it aims to address existing challenges and enhance performance. Additionally, the research surveys diverse application domains, such as drone-based object detection and traffic sign recognition, to demonstrate the practical relevance and adaptability of embedded vision technology across sectors. Through synthesizing key insights, including the significance of popular algorithms like YOLO (You Only Look Once) and the impact of large datasets on performance improvement, the study seeks to provide a comprehensive understanding of embedded vision technology. Ultimately, it aims to guide future efforts in this field, facilitating the creation of innovative solutions for real-world challenges.

Keywords: Embedded Systems, Computer Vision, Object Recognition, Object Tracking, Artificial intelligence