Score refinement for confidence-based 3D multi-object tracking

Benbarka, Nuri; Schröder, Jona; Zell, Andreas

doi:10.1109/iros51168.2021.9636032

Cited by 39 publications

(15 citation statements)

References 19 publications

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“…3D Object Tracking 3D object tracking models tracklets of multiple objects along multi-frame LIDAR. Most previous methods [2,9,51] directly use the Kalman filter upon detection results, such as AB3DMOT [51]. CenterPoint [57] predicts the velocities to associate object centers through multiple frames, following CenterTrack [60].…”

Section: Efficiency On Argoverse2mentioning

confidence: 99%

VoxelNeXt: Fully Sparse VoxelNet for 3D Object Detection and Tracking

Chen¹,

Liu²,

Zhang³

et al. 2023

Preprint

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3D object detectors usually rely on hand-crafted proxies, e.g., anchors or centers, and translate well-studied 2D frameworks to 3D. Thus, sparse voxel features need to be densified and processed by dense prediction heads, which inevitably costs extra computation. In this paper, we instead propose VoxelNext for fully sparse 3D object detection. Our core insight is to predict objects directly based on sparse voxel features, without relying on hand-crafted proxies. Our strong sparse convolutional network Vox-elNeXt detects and tracks 3D objects through voxel features entirely. It is an elegant and efficient framework, with no need for sparse-to-dense conversion or NMS postprocessing. Our method achieves a better speed-accuracy trade-off than other mainframe detectors on the nuScenes dataset. For the first time, we show that a fully sparse voxelbased representation works decently for LIDAR 3D object detection and tracking. Extensive experiments on nuScenes, Waymo, and Argoverse2 benchmarks validate the effectiveness of our approach. Without bells and whistles, our model outperforms all existing LIDAR methods on the nuScenes tracking test benchmark. Code and models are available at github.com/dvlab-research/VoxelNeXt.

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Section: Efficiency On Argoverse2mentioning

confidence: 99%

VoxelNeXt: Fully Sparse VoxelNet for 3D Object Detection and Tracking

Chen¹,

Liu²,

Zhang³

et al. 2023

Preprint

View full text Add to dashboard Cite

show abstract

“…The tracklets that lose their targets for a number of frames (usually fewer than 5) are terminated. Authors in [ 4 , 27 ] recommended that tracks are initiated and terminated based on their confidence score value, which is calculated from the confidence measurement of their related detections. Nevertheless, predictors that are not related with fresh detections are permanently terminated.…”

Section: Related Workmentioning

confidence: 99%

“…As a result, this work proposes an improved and reliable MOT method for point cloud scenarios. Previously developed three dimension (3D) multiple object tracking (3D MOT) algorithms [ 4 , 5 , 6 , 7 , 8 , 9 ] adopt the tracking-by-detection pattern. Across frames, the tracklets depend directly on the 3D bounding boxes from 3D detectors.…”

Section: Introductionmentioning

confidence: 99%

3D-DIoU: 3D Distance Intersection over Union for Multi-Object Tracking in Point Cloud

Mohammed

Razak

Rahman

2023

Sensors

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Multi-object tracking (MOT) is a prominent and important study in point cloud processing and computer vision. The main objective of MOT is to predict full tracklets of several objects in point cloud. Occlusion and similar objects are two common problems that reduce the algorithm’s performance throughout the tracking phase. The tracking performance of current MOT techniques, which adopt the ‘tracking-by-detection’ paradigm, is degrading, as evidenced by increasing numbers of identification (ID) switch and tracking drifts because it is difficult to perfectly predict the location of objects in complex scenes that are unable to track. Since the occluded object may have been visible in former frames, we manipulated the speed and location position of the object in the previous frames in order to guess where the occluded object might have been. In this paper, we employed a unique intersection over union (IoU) method in three-dimension (3D) planes, namely a distance IoU non-maximum suppression (DIoU-NMS) to accurately detect objects, and consequently we use 3D-DIoU for an object association process in order to increase tracking robustness and speed. By using a hybrid 3D DIoU-NMS and 3D-DIoU method, the tracking speed improved significantly. Experimental findings on the Waymo Open Dataset and nuScenes dataset, demonstrate that our multistage data association and tracking technique has clear benefits over previously developed algorithms in terms of tracking accuracy. In comparison with other 3D MOT tracking methods, our proposed approach demonstrates significant enhancement in tracking performances.

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“…The aforementioned trackers terminate or initiate tracks based on hard-coded rules that might be too rigid for the MOT application. Score refinement is proposed in confidence-based 3D MOT [14] for track maintenance, where the tracker achieves a low ID switch score and track fragmentation score. RFS-M3 [15] firstly applies RFS-based methods, specifically PMBM filter, to the LiDAR 3D MOT problem.…”

Section: Related Workmentioning

confidence: 99%

GNN-PMB: A Simple but Effective Online 3D Multi-Object Tracker without Bells and Whistles

Liu¹,

Bai²,

Xia³

et al. 2022

Preprint

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Multi-object tracking (MOT) is among crucial applications in modern advanced driver assistance systems (ADAS) and autonomous driving (AD) systems. Most solutions to MOT are based on random vector Bayesian filters like global nearest neighbor (GNN) plus rule-based heuristical track maintenance. With the development of random finite set (RFS) theory, the RFS Bayesian filters have been applied in MOT tasks for ADAS and AD systems recently. However, their usefulness in the real traffic is open to doubt due to computational cost and implementation complexity. In this paper, it is revealed that GNN with rulebased heuristic track maintenance is insufficient for LiDARbased MOT tasks in ADAS and AD systems. This judgement is illustrated by systematically comparing several different multipoint object filter-based tracking frameworks, including traditional random vector Bayesian filters with rule-based heuristical track maintenance and RFS Bayesian filters. Moreover, a simple and effective tracker, namely Poisson multi-Bernoulli filter using global nearest neighbor (GNN-PMB) tracker, is proposed for LiDAR-based MOT tasks. The proposed GNN-PMB tracker achieves competitive results in nuScenes test dataset, and shows superior tracking performance over other state-of-the-art LiDAR only trackers and LiDAR and camera fusion-based trackers.

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Score refinement for confidence-based 3D multi-object tracking

Cited by 39 publications

References 19 publications

VoxelNeXt: Fully Sparse VoxelNet for 3D Object Detection and Tracking

VoxelNeXt: Fully Sparse VoxelNet for 3D Object Detection and Tracking

3D-DIoU: 3D Distance Intersection over Union for Multi-Object Tracking in Point Cloud

GNN-PMB: A Simple but Effective Online 3D Multi-Object Tracker without Bells and Whistles

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