Real-Time 3D Object Detection Using InnovizOne LiDAR and Low-Power Hailo-8 AI Accelerator

Object detection is a significant field in autonomous driving. Popular sensors for this task include cameras and LiDAR sensors. LiDAR sensors offer several advantages, such as insensitivity to light changes, like in a dark setting and the ability to provide 3D information in the form of point clouds, which include the ranges of objects. However, 3D detection methods, such as PointPillars, typically require high-power hardware. Additionally, most common spinning LiDARs are sparse and may not achieve the desired quality of object detection in front of the car. In this paper, we present the feasibility of performing real-time 3D object detection of cars using 3D point clouds from a LiDAR sensor, processed and deployed on a low-power Hailo-8 AI accelerator. The LiDAR sensor used in this study is the InnovizOne sensor, which captures objects in higher quality compared to spinning LiDAR techniques, especially for distant objects. We successfully achieved real-time inference at a rate of approximately 5Hz with a high accuracy of 0.91% F1 score, with only -0.2% degradation compared to running the same model on an NVIDIA GeForce RTX 2080 Ti. This work demonstrates that effective real-time 3D object detection can be achieved on low-cost, low-power hardware, representing a significant step towards more accessible autonomous driving technologies. The source code and the pre-trained models are available at https://github.com/AIROTAU/ PointPillarsHailoInnoviz/tree/main

CLRmatchNet: Enhancing Curved Lane Detection with Deep Matching Process

Lane detection plays a crucial role in autonomous driving by providing vital data to ensure safe navigation. Modern algorithms rely on anchor-based detectors, which are then followed by a label assignment process to categorize training detections as positive or negative instances based on learned geometric attributes. The current methods, however, have limitations and might not be optimal since they rely on predefined classical cost functions that are based on a low-dimensional model. Our research introduces MatchNet, a deep learning sub-module-based approach aimed at enhancing the label assignment process. Integrated into a state-of-the-art lane detection network like the Cross Layer Refinement Network for Lane Detection (CLRNet), MatchNet replaces the conventional label assignment process with a sub-module network. This integration results in significant improvements in scenarios involving curved lanes, with remarkable improvement across all backbones of +2.8% for ResNet34, +2.3% for ResNet101, and +2.96% for DLA34. In addition, it maintains or even improves comparable results in other sections. Our method boosts the confidence level in lane detection, allowing an increase in the confidence threshold. The code will be available soon: https://github.com/sapirkontente/CLRmatchNet.git

Strong-TransCenter: Improved Multi-Object Tracking based on Transformers with Dense Representations

Transformer networks have been a focus of research in many fields in recent years, being able to surpass the state-of-the-art performance in different computer vision tasks. A few attempts have been made to apply this method to the task of Multiple Object Tracking (MOT), among those the state-of-the-art was TransCenter, a transformer-based MOT architecture with dense object queries for accurately tracking all the objects while keeping reasonable runtime. TransCenter is the first center-based transformer framework for MOT, and is also among the first to show the benefits of using transformer-based architectures for MOT. In this paper we show an improvement to this tracker using post processing mechanism based in the Track-by-Detection paradigm: motion model estimation using Kalman filter and target Re-identification using an embedding network. Our new tracker shows significant improvements in the IDF1 and HOTA metrics and comparable results on the MOTA metric (70.9%, 59.8% and 75.8% respectively) on the MOTChallenge MOT17 test dataset and improvement on all 3 metrics (67.5%, 56.3% and 73.0%) on the MOT20 test dataset. Our tracker is currently ranked first among transformer-based trackers in these datasets. The code is publicly available at: https://github.com/amitgalor18/STC_Tracker

BoT-SORT: Robust Associations Multi-Pedestrian Tracking

The goal of multi-object tracking (MOT) is detecting and tracking all the objects in a scene, while keeping a unique identifier for each object. In this paper, we present a new robust state-of-the-art tracker, which can combine the advantages of motion and appearance information, along with camera-motion compensation, and a more accurate Kalman filter state vector. Our new trackers BoT-SORT, and BoT-SORT-ReID rank first in the datasets of MOTChallenge [29, 11] on both MOT17 and MOT20 test sets, in terms of all the main MOT metrics: MOTA, IDF1, and HOTA. For MOT17: 80.5 MOTA, 80.2 IDF1, and 65.0 HOTA are achieved. The source code and the pre-trained models are available at https://github.com/NirAharon/BOT-SORT