## 1 Introduction  
In contemporary society, various video sources, particularly CCTV, dashcams, and smartphones, are rapidly proliferating and widely employed across diverse fields such as public safety, traffic management, security systems, and autonomous vehicles [1; 2; 3; 4; 5; 6; 7]. CCTV is predominantly installed in public spaces, commercial facilities, residential areas, and beyond, while dashcams are primarily utilized for recording road situations and accidents [8; 9; 10; 11; 12; 13; 14]. Smartphones serve as portable video recording devices, enabling the swift documentation of events occurring in the vicinity [15; 16; 17]. In modern society, various video sources are employed for purposes such as enhancing safety, managing traffic flow, and investigating accidents. The innovative development in object detection technology plays a crucial role in advancing these video data applications [18; 19; 20; 21; 22; 23; 24; 25; 26; 27; 28; 29]. The progress in object detection technology positively impacts diverse fields,including public safety and security, efficiency improvement in traffic systems, and the enhancement of autonomous vehicle performance [4; 5; 6; 7; 8; 9; 10; 11; 12; 13; 14]. This study compares the object detection performance of CCTV, dashcams, and smartphone images, analyzing the influence of each device type on the detection accuracy of different devices. Real-time object detection is a key requirement for various applications, such as safety and security, traffic flow management, and emergency response, using CCTV, dashcam, and smartphone videos [22; 23; 26]. To address this, this experiment employs the YOLOv8 model, known for its superior performance in terms of speed and parameters [30]. However, real-time object detection algorithms like YOLO, while efficient in speed and parameter count, may have lower detection rates compared to heavier models. Therefore, this study aims to achieve performance improvement while maintaining lightweight characteristics. Additionally, the field of object detection has seen significant progress with the advancement of computer vision and machine learning technologies. Nevertheless, accurate and reliable object detection relies heavily on meticulous labeling, a process prone to difficulty, time consumption, and human errors. In response to these challenges, this study aims to explore automated methods for efficient object detection. This study proposes utilizing Grounding DINO [29] to enhance object detection performance in CCTV, dashcam, and smartphone videos and alleviate the difficulties of manual annotation through the use of auto-labeling technology. Grounding DINO serves as a high-performance zero-shot object detector capable of efficient auto-labeling. However, it may not be suitable for real-time object detection scenarios due to slower processing speeds compared to YOLO [26]. This study conducts object detection experiments using publicly available labels and employs Grounding DINO as a teacher model for auto-labeling. The generated labels are then used to train [PERSON] as a student model for object detection experiments, allowing for a comparative analysis of performance. In conclusion, this study affirms that there is no discernible performance degradation when employing automatically generated labels for object detection. The synergistic use of auto-labeling and manual labeling, employing a mixed-method approach, proves to be an effective strategy for performance enhancement. This research delves into the ways in which auto-labeling enhances the accuracy and efficiency of object detection in comparison to manual labeling. Furthermore, it investigates how different device types influence the detection accuracy across various devices. The findings underscore the significant contribution of auto-labeling technology, specifically Grounding DINO, to efficiency and performance improvement within the realm of object detection.