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Object detection and tracking using TSM-EFFICIENTDET and JS-KM in adverse weather conditions

Article type: Research Article

Authors: Arulalan, V.a; * | Premanand, V.b | Kumar, Dhananjayc

Affiliations: [a] Department of Computing Technologies, SRM Institute of Science and Technology, KTR Campus, Tamil Nadu, India | [b] Department of Computer Science and Engineering, Vellore Institute Technology, Chennai Campus, Tamil Nadu, India | [c] Department of Information Technology, Anna University, MIT Campus, India

Correspondence: [*] Corresponding author. V. Arulalan, Department of Computing Technologies, SRM Institute of Science and Technology, KTR Campus, Tamil Nadu, India. Email: [email protected].

Abstract: An efficient model to detect and track the objects in adverse weather is proposed using Tanh Softmax (TSM) EfficientDet and Jaccard Similarity based Kuhn-Munkres (JS-KM) with Pearson-Retinex in this paper. The noises were initially removed using Differential Log Energy Entropy adapted Wiener Filter (DLE-WF). The Log Energy Entropy value was calculated between the pixels instead of calculating the local mean of a pixel in the normal Wiener filter. Also, the segmentation technique was carried out using Fringe Binarization adapted K-Means Algorithm (FBKMA). The movement of segmented objects was detected using the optical flow technique, in which the optical flow was computed using the Horn-Schunck algorithm. After motion estimation, the final step in the proposed system is object tracking. The motion-estimated objects were treated as the target that is initially in the first frame. The target was tracked by JS-KM algorithm in the subsequent frame. At last, the experiential evaluation is conducted to confirm the proposed model’s efficacy. The outcomes of Detection in Adverse Weather Nature (DAWN) dataset proved that in comparison to the prevailing models, a better performance was achieved by the proposed methodology.

Keywords: Object detection, adverse weather, weiner filter, object tracking, Retinex

DOI: 10.3233/JIFS-233623

Journal: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 1, pp. 2399-2413, 2024

Published: 10 January 2024
Price: EUR 27.50

Detecting and Tracking Objects in Challenging Weather Conditions Using Advanced Models

What is it about?

The paper presents an advanced model for detecting and tracking objects in adverse weather conditions. It proposes the use of TSM-EFFICIENTDET and JS-KM with Pearson-Retinex to address the challenges posed by weather conditions such as mist, fog, and haze. The model aims to enhance object detection and tracking accuracies under adverse weather conditions by employing innovative techniques and algorithms.

Why is it important?

The research is important because it addresses a critical challenge in computer vision and surveillance systems. Adverse weather conditions can significantly impact the accuracy of object detection and tracking in video sequences. By proposing an advanced model that specifically targets these challenges, the research aims to improve the reliability and effectiveness of object detection and tracking systems, particularly in real-world scenarios where adverse weather conditions can interfere with traditional methodologies. This has implications for various applications, including surveillance, autonomous vehicles, and robotics, where accurate object detection and tracking are essential.

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