Relation3DMOT: Exploiting Deep Affinity for 3D Multi-Object Tracking from View Aggregation

Chen, Can; Fragonara, Luca Zanotti; Tsourdos, Antonios

doi:10.3390/s21062113

Cited by 3 publications

(3 citation statements)

References 47 publications

(71 reference statements)

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“…However, due to the increase of additional modules and the independent execution of each module, the inference speed of the improved model has dropped significantly. Considering the feature interaction between detected objects in the different frames, Bochinski et al [15] fused the appearance feature and the motion feature captured from 2D RGB images and 3D point clouds to better exploit the correlation between each pair of objects in the adjacent frames. Moreover, potential sharing structure of multi-task is not considered by the above-mentioned models.…”

Section: Multi-object Trackingmentioning

confidence: 99%

ECCNet: Efficient chained centre network for real‐time multi‐category vehicle tracking and vehicle speed estimation

Yang

Jiang

et al. 2022

IET Intelligent Trans Sys

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To achieve the intelligent perception of traffic video and overcome the dispersion of computing modules and the separation processing of multiple tasks, efficient chained centre network (ECCNet) is proposed as a unified framework, accomplishing detection, vehicle classification, tracking, and vehicle speed estimation simultaneously. First, for the speedaccuracy trade-off, CA-CenterNet is presented, which can detect vehicles and classify vehicle types to serve cross-frame tasks more accurately by embedding coordinate attention. Second, 3D convolution is employed to construct a self-adaptive branch for data association and speed estimation, respectively. This self-adaptive approach leverages the power of deep learning to enhance tracking performance and avoid camera calibration via capturing motion information across frames. Moreover, a chained structure is adopted to reuse the backbone feature map. The spatio-temporal information of adjacent frames can be extracted at almost no additional cost. Finally, the above single-frame and cross-frame tasks are integrated into a unified multi-task collaborative optimization model. The effectiveness of ECCNet is verified with experiments on the UA-DETRAC dataset. ECCNet achieves 55.5% MOTA, 0.76 F1 value, and 3.10 MAE with an inference speed of 32.5 Hz in the tracking, classification, and speed estimation tasks, respectively.

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Section: Multi-object Trackingmentioning

confidence: 99%

ECCNet: Efficient chained centre network for real‐time multi‐category vehicle tracking and vehicle speed estimation

Yang

Jiang

et al. 2022

IET Intelligent Trans Sys

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“…Chen et al's system is an autonomous system [130], which used 3D information from 3D point clouds [131] and relation conv for pair of objects correlation. Wang et al proposed unsupervised learning with a Siamese correlation filter network [132], which uses a multi-frame validation scheme and cost-sensitive loss and which have real-time speed using unsupervised learning.…”

Section: Affinitymentioning

confidence: 99%

Multiple Object Tracking in Deep Learning Approaches: A Survey

et al. 2021

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Object tracking is a fundamental computer vision problem that refers to a set of methods proposed to precisely track the motion trajectory of an object in a video. Multiple Object Tracking (MOT) is a subclass of object tracking that has received growing interest due to its academic and commercial potential. Although numerous methods have been introduced to cope with this problem, many challenges remain to be solved, such as severe object occlusion and abrupt appearance changes. This paper focuses on giving a thorough review of the evolution of MOT in recent decades, investigating the recent advances in MOT, and showing some potential directions for future work. The primary contributions include: (1) a detailed description of the MOT’s main problems and solutions, (2) a categorization of the previous MOT algorithms into 12 approaches and discussion of the main procedures for each category, (3) a review of the benchmark datasets and standard evaluation methods for evaluating the MOT, (4) a discussion of various MOT challenges and solutions by analyzing the related references, and (5) a summary of the latest MOT technologies and recent MOT trends using the mentioned MOT categories.

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“…Recent online 3D MOT methods often follow a tracking-by-detection pipeline with two steps: (1) Given trajectories associated up to the last frame and detections in the current frame, an affinity matrix is computed, where each entry represents the similarity value between a past trajectory and a current detection; (2) Given the affinity matrix, the Hungarian algorithm [23] is used to obtain a locally optimal matching, which entails making a hard assignment about which past trajectory a current detection is assigned to, so that trajectories can be updated to the current frame. Though significant progress has been made recently for the first step, for example, by improving the affinity matrix estimation using Graph Neural Networks [9], [24], [25] and multi-modal feature learning [1], [2], step two has largely remained the same. In other words, modern 3D MOT methods typically generate a single set of trajectories via the Hungarian algorithm at inference time, which induces tracking errors that can be detrimental to prediction.…”

Section: Related Workmentioning

confidence: 99%

MTP: Multi-Hypothesis Tracking and Prediction for Reduced Error Propagation

Weng¹,

Ivanovic²,

Pavone³

2021

Preprint

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Recently, there has been tremendous progress in developing each individual module of the standard perceptionplanning robot autonomy pipeline, including detection, tracking, prediction of other agents' trajectories, and ego-agent trajectory planning. Nevertheless, there has been less attention given to the principled integration of these components, particularly in terms of the characterization and mitigation of cascading errors. This paper addresses the problem of cascading errors by focusing on the coupling between the tracking and prediction modules. First, by using state-of-the-art tracking and prediction tools, we conduct a comprehensive experimental evaluation of how severely errors stemming from tracking can impact prediction performance. On the KITTI and nuScenes datasets, we find that predictions consuming tracked trajectories as inputs (the typical case in practice) can experience a significant (even order of magnitude) drop in performance in comparison to the idealized setting where ground truth past trajectories are used as inputs. To address this issue, we propose a multi-hypothesis tracking and prediction framework. Rather than relying on a single set of tracking results for prediction, our framework simultaneously reasons about multiple sets of tracking results, thereby increasing the likelihood of including accurate tracking results as inputs to prediction. We show that this framework improves overall prediction performance over the standard single-hypothesis tracking-prediction pipeline by up to 34.2% on the nuScenes dataset, with even more significant improvements (up to ∼70%) when restricting the evaluation to challenging scenarios involving identity switches and fragments -all with a relatively minor computation overhead. Our project page is here: https://www.xinshuoweng.com/projects/MTP.

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Relation3DMOT: Exploiting Deep Affinity for 3D Multi-Object Tracking from View Aggregation

Cited by 3 publications

References 47 publications

ECCNet: Efficient chained centre network for real‐time multi‐category vehicle tracking and vehicle speed estimation

ECCNet: Efficient chained centre network for real‐time multi‐category vehicle tracking and vehicle speed estimation

Multiple Object Tracking in Deep Learning Approaches: A Survey

MTP: Multi-Hypothesis Tracking and Prediction for Reduced Error Propagation

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