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Probabilistic object tracking by low power microcontrollers

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Abstract

Low power microcontrollers have become widely available. Hence, they have been used in several stand-alone applications in which the developed system depends on battery or energy harvesting module. One such application is surveillance aiming to observe a selected region or target in time. Due to the complexity of the problem and real-time constraints in operation, several object trackers have been proposed in literature. An object tracker produces the trajectory of an object from a given image sequence. To do so, two major steps are taken as object representation and trajectory prediction. Here, the computation load for tracking and object representation strength plays adversary effects most of the time. Moreover, the overall system to be deployed in a remote location casts serious limitations on the tracking method to be used. Therefore, we propose a probabilistic object representation-based object tracking method to work on low power Arm Cortex-M4 and -M7 core microcontrollers in this study. The proposed method aims to represent the object to be tracked as simple as possible. On the other hand, the method provides an effective way of describing the object to be tracked. Therefore, the novelty of the proposed method is adding a simple yet flexible probabilistic object representation method to the tracking framework. The probabilistic object representation method can be easily merged with the Bayesian framework which is extensively used in trajectory prediction. To do so, we use the particle filter based Bayesian tracking method. As we form the overall system for object tracking, we compare it with similar methods in the literature under real-time constraints. We provide experimental results to show the strengths and weaknesses of the proposed method in comparison with the existing ones.

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Acknowledgements

This work is supported by Marmara University under project no FEN-A-170419-0119.

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Authors and Affiliations

  1. Yeditepe University, Istanbul, Turkey

    Hüseyin Büyükeşmeli

  2. Department of Electrical and Electronics Engineering, Faculty of Engineering, Marmara University, Istanbul, Turkey

    Engin Masazade & Cem Ünsalan

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  1. Hüseyin Büyükeşmeli
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  2. Engin Masazade
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  3. Cem Ünsalan
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Correspondence to Cem Ünsalan.

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Büyükeşmeli, H., Masazade, E. & Ünsalan, C. Probabilistic object tracking by low power microcontrollers. J Real-Time Image Proc 18, 2539–2550 (2021). https://doi.org/10.1007/s11554-021-01139-2

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