A Comparative analysis of Algorithms for Pedestrian Tracking using Drone Vision

Koundinya, Poluri Nikhil and Sanjukumar, NT and Rajalakshmi, P (2021) A Comparative analysis of Algorithms for Pedestrian Tracking using Drone Vision. In: 4th IEEE International Conference on Computing, Power and Communication Technologies, GUCON 2021, 24 September 2021 through 26 September 2021, Kuala Lumpur.

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Abstract

In recent years, there has been an upsurge in the use of drones for various applications such as intelligent transportation, smart agriculture, military, product delivery, etc. With the advancement of high computational edge devices which can support Machine Learning and Deep Learning algorithms, various functions such as object detection and object tracking can be performed in real-time. Though there are many tracking algorithms available for object tracking, there is always a tradeoff between accuracy and their run-time. Executing computationally expensive algorithms is largely bottle-necked by hardware constraints. This paper has compared different object tracking algorithms (both conventional and Deep learning-based) based on tracking accuracy, speed of tracking, and computational complexity of each algorithm. The comparison is based on the accuracy of detection and tracking of an object at the beginning, end, or time of any occlusion scenario in the drone's video. © 2021 IEEE.

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