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ACO–MKFCM: An Optimized Object Detection and Tracking Using DNN and Gravitational Search Algorithm

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Abstract

Object tracking is a dynamic optimization process based on the temporal information related to the previous frames. Proposing a method with higher precision in complex environments is a challenge for researchers in the field of study. In this research, efficient object detection and movement tracking video is proposed. Here we are considering the input video sequence is PETS and Hall monitor video. Kernel fuzzy c-means procedure still endures several downsides, such as reduced convergence rate, obtaining stuck in the local minima as well as at risk to initialization level of sensitivity. Ant colony optimization algorithm is a new population-based optimization technique that has actually been utilized effectively for resolving numerous complicated difficulties. This research suggested a new strategy called ACO–MKFCM to resolve MKFCM initialization issue making use of ant colony optimization (ACO) algorithm to locate optimum first cluster centres for the MKFCM, hence enhance all applications associated fuzzy clustering such as foreground segmentation in image processing. Initially, the background and foreground separation is done by hybridization of modified kernel fuzzy c means algorithm (MKFCM) with ant colony optimization. The recommended new technique is intellectual and also vibrant clustering method for splitting up of non-static object. Then object detection and tracking is done by gravitational search algorithm based deep belief neural network. The implementation will be in MATLAB. The performance of the suggested technique is appraised by means of precision, recall, F-measure, FPR, FNR, PWC, FAR, similarity, specificity, and accuracy. From the empirical effects, the future work outperforms than the state of art work. Here the proposed method attains maximum precision and recall value for both PETS and Hall monitor video when analyzed to the existent algorithm.

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Authors and Affiliations

  1. Sathyabama University, Chennai, Tamil Nadu, India

    T. Mahalingam & M. Subramoniam

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  1. T. Mahalingam
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  2. M. Subramoniam
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Mahalingam, T., Subramoniam, M. ACO–MKFCM: An Optimized Object Detection and Tracking Using DNN and Gravitational Search Algorithm. Wireless Pers Commun 110, 1567–1604 (2020). https://doi.org/10.1007/s11277-019-06802-3

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