Neighboring Predictive Gradient Spatio-Temporal Sequencing Algorithm: An Object Sequencing Algorithm for Logical Ordering of Sparse Object Detections

When object detection is carried out in settings with sparse and irregular data acquisition, conventional sequencing techniques that depend on continuous tracking or dense observations frequently fall short of reconstructing the proper logical sequence of events. This paper introduces the Neighboring Predictive Gradient Spatio-Temporal Sequencing Algorithm (NPGSTSA), a novel framework for determining detected objects’ sequential order in sparse and scarce environments. NPGSTSA leverages the relative y-intercepts of detected objects and their neighboring relations as proxies for sequence position inference. By utilizing the properties of the gradient and its neighboring data object, the algorithm is capable of robustly estimating sequence flow even under severe data sparsity and insufficient data points. To evaluate its effectiveness, we constructed a simulated video-based object detection with varying data sparsity levels and downsampling factors. To access the quantitative assessment of the sequence accuracy, we modified a 1-Wasserstein distance as a measurement metric to demonstrate that NPGSTSA significantly outperforms conventional methods such as First-In-First-Out (FIFO) and cluster-based sequencing. The results confirm the algorithm’s capacity to infer coherent object sequences in data-constrained scenarios.