Comparative studies for YOLOv4 target tracking algorithm performance

Abstract

This paper presents a detailed experimental study on operability of a target tracking system based on YOLOv4 for target detection. This work aimed to determine multi-dimensional regions where the two parameters of YOLOv4 algorithm, detection threshold and IOU threshold, allow obtaining the maximum of performance from the detection algorithm, represented by 100% detection for the overall possible detected targets. Low values of these parameters allow more detections, but also increased the number of misclassifications, counted as wrong detections. On contrast, high values of these parameters increase the detection accuracy, but on the cost of total number of detected targets. Considering that each of these parameters can have at least 9 values, 0.1 to 0.9, resulting in 81 combinations, the identification of local and global maxima and minima and of an operational zone, in which the system performance is stable, could be considered as priorities on context of optimal use of the overall target tracking system. Given the high diversity of graphics representation for the system performance in respect with the variance of these two parameters, each of them influencing in an inter-dependent mode the overall performance of the system, the authors propose that the results of each experimental complex scenario to be consolidated in 3D graph, named decision space, one for each environmental context, in which each point is the result of system efficiency reported to a specific pair of values of the mentioned algorithm parameters. These decision spaces present in aggregated form the performance obtained in a target tracking scenario, related on a specific environment, but considered all possible parameter values. The results can be used as a method of pre calibrating the target tracking system prior of effectively using it for its main purpose.

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