Recent Advances in Embedding Methods for Multi-Object Tracking: A Survey

Wang, Gaoang; Song, Mingli; Hwang, Jenq‐Neng

doi:10.48550/arxiv.2205.10766

Cited by 4 publications

(4 citation statements)

References 284 publications

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“…Since DeepSORT [60], using appearance features to associate detections with the tracklets has become a popular approach in tracking-by-detection MOT [1,17,57,59]. Specifically, the cosine distance between visual embedding vectors is used to construct the association cost matrix.…”

Section: Similarity Measuresmentioning

confidence: 99%

BoostTrack: boosting the similarity measure and detection confidence for improved multiple object tracking

Stanojevic,

Todorovic

2024

Machine Vision and Applications

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Handling unreliable detections and avoiding identity switches are crucial for the success of multiple object tracking (MOT). Ideally, MOT algorithm should use true positive detections only, work in real-time and produce no identity switches. To approach the described ideal solution, we present the BoostTrack, a simple yet effective tracing-by-detection MOT method that utilizes several lightweight plug and play additions to improve MOT performance. We design a detection-tracklet confidence score and use it to scale the similarity measure and implicitly favour high detection confidence and high tracklet confidence pairs in one-stage association. To reduce the ambiguity arising from using intersection over union (IoU), we propose a novel Mahalanobis distance and shape similarity additions to boost the overall similarity measure. To utilize low-detection score bounding boxes in one-stage association, we propose to boost the confidence scores of two groups of detections: the detections we assume to correspond to the existing tracked object, and the detections we assume to correspond to a previously undetected object. The proposed additions are orthogonal to the existing approaches, and we combine them with interpolation and camera motion compensation to achieve results comparable to the standard benchmark solutions while retaining real-time execution speed. When combined with appearance similarity, our method outperforms all standard benchmark solutions on MOT17 and MOT20 datasets. It ranks first among online methods in HOTA metric in the MOT Challenge on MOT17 and MOT20 test sets. We make our code available at https://github.com/vukasin-stanojevic/BoostTrack.

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Section: Similarity Measuresmentioning

confidence: 99%

BoostTrack: boosting the similarity measure and detection confidence for improved multiple object tracking

Stanojevic,

Todorovic

2024

Machine Vision and Applications

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“…These learning strategies are also successfully extended to video representation learning, such as [69,2,60,7]. With much progress made recently, visual representation learning is employed in many real-world applications, such as anomaly detection [77,76], and human-based perception [61,63,62,65]. However, the complex hierarchical relationships among instances are seldom explored in the existing works.…”

Section: Related Workmentioning

confidence: 99%

Hierarchical Semi-Supervised Contrastive Learning for Contamination-Resistant Anomaly Detection

Wang¹,

Zhan²,

Wang³

et al. 2022

Preprint

Self Cite

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Anomaly detection aims at identifying deviant samples from the normal data distribution. Contrastive learning has provided a successful way to sample representation that enables effective discrimination on anomalies. However, when contaminated with unlabeled abnormal samples in training set under semi-supervised settings, current contrastive-based methods generally 1) ignore the comprehensive relation between training data, leading to suboptimal performance, and 2) require fine-tuning, resulting in low efficiency. To address the above two issues, in this paper, we propose a novel hierarchical semi-supervised contrastive learning (HSCL) framework, for contamination-resistant anomaly detection. Specifically, HSCL hierarchically regulates three complementary relations: sample-to-sample, sample-to-prototype, and normal-to-abnormal relations, enlarging the discrimination between normal and abnormal samples with a comprehensive exploration of the contaminated data. Besides, HSCL is an end-to-end learning approach that can efficiently learn discriminative representations without fine-tuning. HSCL achieves state-of-the-art performance in multiple scenarios, such as one-class classification and cross-dataset detection. Extensive ablation studies further verify the effectiveness of each considered relation. The code is available at https://github.com/GaoangW/HSCL.

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“…Nevertheless, the MOT task, especially from the perspective of UAV, presents a multitude of challenges. The UAV-based MOT encounters issues such as image deterioration, alterations in scale, ambiguous object brightness and immediate tracking [4][5][6].…”

Section: Introductionmentioning

confidence: 99%

An Asymmetric Feature Enhancement Network for Multiple Object Tracking of Unmanned Aerial Vehicle

Ma,

Liu,

Qin

et al. 2023

Remote Sensing

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Multiple object tracking (MOT) in videos captured by unmanned aerial vehicle (UAV) is a fundamental aspect of computer vision. Recently, the one-shot tracking paradigm integrates the detection and re-identification (ReID) tasks, striking a balance between tracking accuracy and inference speed. This paradigm alleviates task conflicts and achieves remarkable results through various feature decoupling methods. However, in challenging scenarios like drone movements, lighting changes and object occlusion, it still encounters issues with detection failures and identity switches. In addition, traditional feature decoupling methods directly employ channel-based attention to decompose the detection and ReID branches, without a meticulous consideration of the specific requirements of each branch. To address the above problems, we introduce an asymmetric feature enhancement network with a global coordinate-aware enhancement (GCAE) module and an embedding feature aggregation (EFA) module, aiming to optimize the two branches independently. On the one hand, we develop the GCAE module for the detection branch, which effectively merges rich semantic information within the feature space to improve detection accuracy. On the other hand, we introduce the EFA module for the ReID branch, which highlights the significance of pixel-level features and acquires discriminative identity embedding through a local feature aggregation strategy. By efficiently incorporating the GCAE and EFA modules into the one-shot tracking pipeline, we present a novel MOT framework, named AsyUAV. Extensive experiments have demonstrated the effectiveness of our proposed AsyUAV. In particular, it achieves a MOTA of 38.3% and IDF1 of 51.7% on VisDrone2019, and a MOTA of 48.0% and IDF1 of 67.5% on UAVDT, outperforming existing state-of-the-art trackers.

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Recent Advances in Embedding Methods for Multi-Object Tracking: A Survey

Cited by 4 publications

References 284 publications

BoostTrack: boosting the similarity measure and detection confidence for improved multiple object tracking

BoostTrack: boosting the similarity measure and detection confidence for improved multiple object tracking

Hierarchical Semi-Supervised Contrastive Learning for Contamination-Resistant Anomaly Detection

An Asymmetric Feature Enhancement Network for Multiple Object Tracking of Unmanned Aerial Vehicle

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