An Improved Object Tracking Technique for Remote Weapon Station Using Yolov5_Deepsort_Dlib Architecture

Document Type : Original Article

Authors

1 Computer Science Department, Air Force Institute of Technology, Kaduna State, Nigeria

2 Computer Science Department, Nigerian Defence Academy, Nigeria

3 Computer Science Department, Nigerian Defence Academy, Kaduna, Nigeria

Abstract

This paper introduces an advanced tracking object architecture named DeepSORT_YOLOv5_Dlib. Building upon the DeepSORT_YOLOv3 framework, the study [1] integrates the Digital Library's correlation tracker into the traditional DeepSORT_YOLOv3 to minimize identity switches. Notably, the architecture is designed to operate in parallel, enhancing its operational speed. Experimental results indicate that the proposed approach outperforms the conventional DeepSort_YOLOv3, showcasing reduced identity switches and increased operational speed across various video testing scenarios. The custom model employed in this study adopts a confidence threshold of 0.2 and an image size of 416 x 416, consistent with the training size. To boost detection within YOLOv5, the model incorporates the Slicing Aided Hyper Inference (SAHI) technique. The overall inference speed in this study reaches 314.8fps, a notable improvement compared to Dang's 218.6fps. Evaluation using the COCO dataset demonstrates the model's precision at 0.98 and a recall of 0.81. Additionally, the proposed custom model exhibits a MOTA of 0.86, surpassing the benchmark's 0.83. Notably, our model achieves a significantly lower identity switch count of 1881 compared to the benchmark's count of 2288. Furthermore, it outperforms the benchmark in object detection capabilities. By incorporating SAHI inference with YOLOv5, the study enhances detection accuracy, resulting in an overall tracking accuracy improvement from 56% to 79%. These findings highlight the efficacy of the proposed custom model in achieving superior performance in object tracking and detection.

Keywords


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