Three-dimensional (3D) object tracking is critical in 3D computer vision. It has applications in autonomous driving, robotics, and human–computer interaction. However, methods for using multimodal information among objects to increase multi-object detection and tracking (MOT) accuracy remain a critical focus of research. Therefore, we present a multimodal MOT framework for autonomous driving boost correlation multi-object detection and tracking (BcMODT) in this research study to provide more trustworthy features and correlation scores for real-time detection tracking using both camera and LiDAR measurement data. Specifically, we propose an end-to-end deep neural network using 2D and 3D data for joint object detection and association. A new 3D mixed IoU (3D-MiIoU) computational module is also developed to acquire more precise geometric affinity by increasing the aspect ratio and length-to-height ratio between linked frames. Meanwhile, a boost correlation feature (BcF) module is proposed for the affinity calculation of the appearance of similar objects, which comprises an appearance affinity calculation module for similar objects in adjacent frames that are calculated directly using the feature distance and feature direction’s similarity. The KITTI tracking benchmark shows that our method outperforms other methods with respect to tracking accuracy.
Feature selection is a crucial process in machine learning and data mining that identifies the most pertinent and valuable features in a dataset. It enhances the efficacy and precision of predictive models by efficiently reducing the number of features. This reduction improves classification accuracy, lessens the computational burden, and enhances overall performance. This study proposes the improved binary golden jackal optimization (IBGJO) algorithm, an extension of the conventional golden jackal optimization (GJO) algorithm. IBGJO serves as a search strategy for wrapper-based feature selection. It comprises three key factors: a population initialization process with a chaotic tent map (CTM) mechanism that enhances exploitation abilities and guarantees population diversity, an adaptive position update mechanism using cosine similarity to prevent premature convergence, and a binary mechanism well-suited for binary feature selection problems. We evaluated IBGJO on 28 classical datasets from the UC Irvine Machine Learning Repository. The results show that the CTM mechanism and the position update strategy based on cosine similarity proposed in IBGJO can significantly improve the Rate of convergence of the conventional GJO algorithm, and the accuracy is also significantly better than other algorithms. Additionally, we evaluate the effectiveness and performance of the enhanced factors. Our empirical results show that the proposed CTM mechanism and the position update strategy based on cosine similarity can help the conventional GJO algorithm converge faster.
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