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: arulalav@srmist.edu.in.
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.
