Real driving scenarios,due to occlusions anddisturbances,provide disordered and noisy measurements,which makes the task of multi-objecttracking quite *** approach is to finddeterministic data association;however,it...
详细信息
Real driving scenarios,due to occlusions anddisturbances,provide disordered and noisy measurements,which makes the task of multi-objecttracking quite *** approach is to finddeterministic data association;however,it has unstable performance in high clutter *** paper proposes a novel probabilistic tracklet-enhancedmultiple object tracker(PTMOT),which integrates Poisson multi-Bernoulli mixture(PMBM)filter with confidence of *** proposed method is able to realize efficient and robust probabilistic association for 3d multi-object tracking(MOT)and improve the PMBM filter’s continuity by smoothing single target hypothesis with global *** consists of two key ***,the PMBM tracker based on sets of tracklets is implemented to realize probabilistic fusion of disordered ***,the confidence of tracklets is smoothed through a smoothing-while-filtering *** MOT tests on nuScenes trackingdataset demonstrate that the proposed method achieves superior performance in different modalities.
Autonomous driving (Ad) promises an efficient, comfortable and safe driving experience. Nevertheless, fatalities involving vehicles equipped with Automateddriving Systems (AdSs) are on the rise, especially those rela...
详细信息
Autonomous driving (Ad) promises an efficient, comfortable and safe driving experience. Nevertheless, fatalities involving vehicles equipped with Automateddriving Systems (AdSs) are on the rise, especially those related to the perception module of the vehicle. This paper presents a real-time and power-efficient 3dmulti-objectdetection andtracking (dAMOT) method proposed for Intelligent Vehicles (IV) applications, allowing the vehicle to track 360 degrees surrounding objects as a preliminary stage to perform trajectory forecasting to prevent collisions and anticipate the ego-vehicle to future traffic scenarios. First, we present our dAMOT pipeline based on Fast Encoders for objectdetection and a combination of a 3d Kalman Filter and Hungarian Algorithm, used for state estimation anddata association respectively. We extend our previous work ellaborating a preliminary version of sensor fusion baseddAMOT, merging the extracted features by a Convolutional Neural Network (CNN) using camera information for long-term re-identification and obstacles retrieved by the 3dobjectdetector. Both pipelines exploit the concepts of lightweight Linux containers using the docker approach to provide the system with isolation, flexibility and portability, and standard communication in robotics using the Robot Operating System (ROS). Second, both pipelines are validated using the recently proposed KITTI-3dMOT evaluation tool that demonstrates the full strength of 3d localization andtracking of a MOT system. Finally, the most efficient architecture is validated in some interesting traffic scenarios implemented in the CARLA (Car Learning to Act) open-source driving simulator and in our real-world autonomous electric car using the NVIdIA AGX Xavier, an AI embedded system for autonomous machines, studying its performance in a controlled but realistic urban environment with real-time execution (results).
multi-objecttracking (MOT) systems typically rely on objectdetection results for tracking, so the accuracy of the MOT system is significantly affected by the error of the detector. Changes in error usually lead to u...
详细信息
multi-objecttracking (MOT) systems typically rely on objectdetection results for tracking, so the accuracy of the MOT system is significantly affected by the error of the detector. Changes in error usually lead to unstable tracking. Regarding this problem, we proposed an adaptive MOT method based on detection confidence. At first, we use a simple data fusion method to combine the detection results of LidAR and camera to reduce the large number of false detections. And then we used a factor based on confidence to adjust the estimating covariance matrix and measurement covariance matrix adaptively. The algorithm can judge which is more reliable between prediction anddetection, and choose which is more important in the update step. Meanwhile, we set a factor based on confidence to control the search range in the data association module. Our method reduces the impact of detector error while ensuring accuracy and speed, and improves the robustness of the MOT algorithm. Through experiments conducted on the KITTI multi-objecttrackingdataset, our method has demonstrated significant advantages over state-of-the-art (SOTA) methods in terms of both accuracy and processing speed. The results of MOTA for 90.02% and FPS for 262.
This paper presents a Real-Time Bird's Eye View multiobjecttracking (MOT) system pipeline for an Autonomous Electric car, based on Fast Encoders for objectdetection and a combination of Hungarian algorithm and ...
详细信息
ISBN:
(纸本)9781728141497
This paper presents a Real-Time Bird's Eye View multiobjecttracking (MOT) system pipeline for an Autonomous Electric car, based on Fast Encoders for objectdetection and a combination of Hungarian algorithm and Bird's Eye View (BEV) Kalman Filter, respectively used for data association and state estimation. The system is able to analyze 360 degrees around the ego-vehicle as well as estimate the future trajectories of the environment objects, being the essential input for other layers of a self-driving architecture, such as the control or decision-making. First, our system pipeline is described, merging the concepts of online and real-time dATMO (deteccion andtracking of multiple objects), ROS (Robot Operating System) anddocker to enhance the integration of the proposed MOT system in fully-autonomous driving architectures. Second, the system pipeline is validated using the recently proposed KITTI-3dMOT evaluation tool that demonstrates the full strength of 3d localization andtracking of a MOT system. Finally, a comparison of our proposal with other state-of-the-art approaches is carried out in terms of performance by using the mainstream metrics used on MOT benchmarks and the recently proposed integral MOT metrics, evaluating the performance of the tracking system over all detection thresholds.
暂无评论