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Swarm intelligence based object tracking

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A Correction to this article was published on 05 May 2023

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Abstract

Though object tracking is a very old problem still there are several challenges to be solved; for instance, variation of illumination of light, noise, occlusion, sudden start and stop of moving object, shading etc. In this paper we propose a dual approach for object tracking based on optical flow and swarm Intelligence. The optical flow based KLT tracker, tracks the dominant points of the target object from first frame to last frame of a video sequence; whereas swarm Intelligence based PSO tracker simultaneously tracks the boundary information of the target object from second frame to last frame of the same video sequence. The boundary information of the target object is captured by the polygonal approximation of the same. The dual approach to object tracking is inherently robust with respect to the above stated problems. We compare the performance of the proposed dual tracking algorithm with several benchmark datasets and in most of the cases we obtain superior results.

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Data Availability

In this research work we use 3 datasets for our experiment. Below we provide a table which include those 3 dataset’s availability information along with their location. This data availability information properly summarize the data we use in our approach.

Availability of data

Data availability statement

Policy

Dataset-1

The data that support the findings of this study are openly available in Online Object Tracking: A Benchmark at http://cvlab.hanyang.ac.kr/tracker_benchmark/, [86]

All

Dataset-2

The data that support the findings of this study are openly available in Long-Term Visual Object Tracking Benchmark at https://amoudgl.github.io/tlp/, [48]

All

Dataset-3

The data that support the findings of this study are openly available in Object Tracking Evaluation 2012 from The KITTI Vision Benchmark Suite at http://www.cvlibs.net/datasets/kitti/eval_tracking.php, [27]

All

Change history

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    Authors and Affiliations

    1. S.A.Jaipuria College, 10 Raja Naba Krishna Street, Kolklata, West Bengal, India

      Rajesh Misra

    2. GLA University, 17KM Stone, NH2, Mathura, 281406, UP, India

      Kumar S. Ray

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    The original online version of this article was revised: The author name of reference [12] was incorrectly listed as ”Bogdan K” instead of ”Kwolek B” in the original publication of this article.

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    Misra, R., Ray, K.S. Swarm intelligence based object tracking. Multimed Tools Appl 82, 28009–28039 (2023). https://doi.org/10.1007/s11042-023-14343-y

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