arXiv subjects

Yangxintong Lyu

Publications and source records attributed to Yangxintong Lyu.

4 recordsLinked to original sources

arXiv2025

HybridTrack: A Hybrid Approach for Robust Multi-Object Tracking

The evolution of Advanced Driver Assistance Systems (ADAS) has increased the need for robust and generalizable algorithms for multi-object tracking. Traditional statistical model-based tracking methods rely on predefined motion models and assumptions about system noise distributions. Although computationally efficient, they often lack adaptability to varying traffic scenarios and require extensive manual design and parameter tuning. To address these issues, we propose a novel 3D multi-object tracking approach for vehicles, HybridTrack, which integrates a data-driven Kalman Filter (KF) within a tracking-by-detection paradigm. In particular, it learns the transition residual and Kalman gain directly from data, which eliminates the need for manual motion and stochastic parameter modeling. Validated on the real-world KITTI dataset, HybridTrack achieves 82.72% HOTA accuracy, significantly outperforming state-of-the-art methods. We also evaluate our method under different configurations, achieving the fastest processing speed of 112 FPS. Consequently, HybridTrack eliminates the dependency on scene-specific designs while improving performance and maintaining real-time efficiency. The code is publicly available at: https://github.com/leandro-svg/HybridTrack.

cs.CV↗

arXiv2024

A Power-Efficient Hardware Implementation of L-Mul

Multiplication is a core operation in modern neural network (NN) computations, contributing significantly to energy consumption. The linear-complexity multiplication (L-Mul) algorithm is specifically proposed as an approximate multiplication method for emerging NN models, such as large language model (LLM), to reduce the energy consumption and computational complexity of multiplications. However, hardware implementation designs for L-Mul have not yet been reported. Additionally, 8-bit floating-point (FP8), as an emerging data format, offers a better dynamic range compared to traditional 8-bit integer (INT8), making it increasingly popular and widely adopted in NN computations. This paper thus presents a power-efficient FPGAbased hardware implementation (approximate FP8 multiplier) for L-Mul. The core computation is implemented using the dynamic reconfigurable lookup tables and carry chains primitives available in AMD Xilinx UltraScale/UltraScale+ technology. The accuracy and resource utilization of the approximate multiplier are evaluated and analyzed. Furthermore, the FP8 approximate multiplier is deployed in the inference phase of representative NN models to validate its effectiveness.

cs.AR↗

arXiv2024

LAM3D: Leveraging Attention for Monocular 3D Object Detection

Since the introduction of the self-attention mechanism and the adoption of the Transformer architecture for Computer Vision tasks, the Vision Transformer-based architectures gained a lot of popularity in the field, being used for tasks such as image classification, object detection and image segmentation. However, efficiently leveraging the attention mechanism in vision transformers for the Monocular 3D Object Detection task remains an open question. In this paper, we present LAM3D, a framework that Leverages self-Attention mechanism for Monocular 3D object Detection. To do so, the proposed method is built upon a Pyramid Vision Transformer v2 (PVTv2) as feature extraction backbone and 2D/3D detection machinery. We evaluate the proposed method on the KITTI 3D Object Detection Benchmark, proving the applicability of the proposed solution in the autonomous driving domain and outperforming reference methods. Moreover, due to the usage of self-attention, LAM3D is able to systematically outperform the equivalent architecture that does not employ self-attention.

cs.CV↗

arXiv2024

DeepKalPose: An Enhanced Deep-Learning Kalman Filter for Temporally Consistent Monocular Vehicle Pose Estimation

This paper presents DeepKalPose, a novel approach for enhancing temporal consistency in monocular vehicle pose estimation applied on video through a deep-learning-based Kalman Filter. By integrating a Bi-directional Kalman filter strategy utilizing forward and backward time-series processing, combined with a learnable motion model to represent complex motion patterns, our method significantly improves pose accuracy and robustness across various conditions, particularly for occluded or distant vehicles. Experimental validation on the KITTI dataset confirms that DeepKalPose outperforms existing methods in both pose accuracy and temporal consistency.

cs.CV↗