SQE: a Self Quality Evaluation Metric for Parameters Optimization in Multi-Object Tracking

Huang, Yanru; Zhu, Feiyu; Zeng, Zheni; Qiu, Xi; Shen, Yuan; Wu, Jianan

doi:10.1109/cvpr42600.2020.00833

Cited by 9 publications

(6 citation statements)

References 25 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…MOTA is computed based on FP, FN and IDs, and focuses more on detection performance. By comparison, IDF1 better measures the consistency of ID matching [35]. HOTA is an explicit combination of detection score DetA and association score AssA, which balances the effects of performing accurate detection and association into a single unified metric.…”

Section: A Settingmentioning

confidence: 99%

StrongSORT: Make DeepSORT Great Again

Zhao

Song

et al. 2023

IEEE Trans. Multimedia

179

View full text Add to dashboard Cite

Multi-Object Tracking (MOT) has gained lots attention from researchers and achieved remarkable progress in recent years. However, recent studies on MOT tend to use different basic models (e.g, detector and embedding model), training data and training/inference tricks, which makes it difficult to construct a fair comparison between their progress. In this paper, we revisit the classic tracker DeepSORT, and upgrades it from aspects of detection, embedding, and association. The proposed tracker, named StrongSORT, achieves great improvements over DeepSORT, and can serve as a strong and fair baseline for other methods. We also present two lightweight and plugand-play algorithms to solve two "missing" problems in MOT. Firstly, to solve the missing association problem, some works associate short tracklets into complete trajectories with computationally expensive models. Instead, we propose the appearancefree link model (AFLink) to perform global association without appearance information, which achieves a better trade-off between speed and accuracy. Secondly, for the missing detection problem, we propose Gaussian-smoothed interpolation (GSI), which improves the linear interpolation algorithm with Gaussian process regression. Moreover, AFLink and GSI can be plugged into various trackers with a negligible extra computational cost (1.7 ms and 7.1 ms per image, respectively, on MOT17). By integrating StrongSORT with the two algorithms, the final tracker StrongSORT++ achieves SOTA results on multiple benchmarks, i.e., MOT17, MOT20, DanceTrack and KITTI. Codes are available at https://github.com/dyhBUPT/StrongSORT and https://github.com/open-mmlab/mmtracking.

show abstract

Section: A Settingmentioning

confidence: 99%

StrongSORT: Make DeepSORT Great Again

Zhao

Song

et al. 2023

IEEE Trans. Multimedia

179

View full text Add to dashboard Cite

show abstract

“…MOTA is computed based on FP, FN and IDs, and focuses more on detection performance. By comparison, IDF1 better measures the consistency of ID matching [23]. HOTA is an explicit combination of detection score DetA and association score AssA, which balances the effects of performing accurate detection and association into a single unified metric.…”

Section: Datasets and Evaluation Metricsmentioning

confidence: 99%

StrongSORT: Make DeepSORT Great Again

Du¹,

Yang²,

Yang³

et al. 2022

Preprint

View full text Add to dashboard Cite

Existing Multi-Object Tracking (MOT) methods can be roughly classified as tracking-by-detection and joint-detection-association paradigms. Although the latter has elicited more attention and demonstrates comparable performance relative to the former, we claim that the trackingby-detection paradigm is still the optimal solution in terms of tracking accuracy. In this paper, we revisit the classic tracker DeepSORT and upgrade it from various aspects, i.e., detection, embedding and association. The resulting tracker, called StrongSORT, sets new HOTA and IDF1 records on MOT17 and MOT20. We also present two lightweight and plug-and-play algorithms to further refine the tracking results. Firstly, an appearance-free link model (AFLink) is proposed to associate short tracklets into complete trajectories. To the best of our knowledge, this is the first global link model without appearance information. Secondly, we propose Gaussian-smoothed interpolation (GSI) to compensate for missing detections. Instead of ignoring motion information like linear interpolation, GSI is based on the Gaussian process regression algorithm and can achieve more accurate localizations. Moreover, AFLink and GSI can be plugged into various trackers with a negligible extra computational cost (591.9 and 140.9 Hz, respectively, on MOT17). By integrating StrongSORT with the two algorithms, the final tracker Strong-SORT++ ranks first on MOT17 and MOT20 in terms of HOTA and IDF1 metrics and surpasses the second-place one by 1.3 -2.2. Code will be released soon.

show abstract

“…Unlike IDF1, there are no differences between ResNet-50 and MuDeep for re-identification; both extended-ResNet-50 and extended-MuDeep achieve an average MOTA of 62.7%. Compared to MOTA, IDF1 is better at expressing the consistency of ID matching, by measuring how long the identification is correct (Huang et al, 2020), which means that the use of MuDeep improves the ID matching of the tracked chicks. The reason why the tracking results of Sequence 3 are much worse than the results of the other two test sequences, i.e., around 30% worse in IDF1 and around 70% worse in MOTA compared to 1c, is the high number of false-positive detections.…”

Section: Mudeepmentioning

confidence: 99%

Deep Learning-Based Tracking of Multiple Objects in the Context of Farm Animal Ethology

Ali

Dorozynski

Stracke

et al. 2022

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

View full text Add to dashboard Cite

Abstract. Automatic detection and tracking of individual animals is important to enhance their welfare and to improve our understanding of their behaviour. Due to methodological difficulties, especially in the context of poultry tracking, it is a challenging task to automatically recognise and track individual animals. Those difficulties can be, for example, the similarity of animals of the same species which makes distinguishing between them harder, or sudden changes in their body shape which may happen due to putting on or spreading out the wings in a very short period of time. In this paper, an automatic poultry tracking algorithm is proposed. This algorithm is based on the well-known tracktor approach and tackles multi-object tracking by exploiting the regression head of the Faster R-CNN model to perform temporal realignment of object bounding boxes. Additionally, we use a multi-scale re-identification model to improve the re-association of the detected animals. For evaluating the performance of the proposed method in this study, a novel dataset consisting of seven image sequences that show chicks in an average pen farm in different stages of growth is used.

show abstract

SQE: a Self Quality Evaluation Metric for Parameters Optimization in Multi-Object Tracking

Cited by 9 publications

References 25 publications

StrongSORT: Make DeepSORT Great Again

StrongSORT: Make DeepSORT Great Again

StrongSORT: Make DeepSORT Great Again

Deep Learning-Based Tracking of Multiple Objects in the Context of Farm Animal Ethology

Contact Info

Product

Resources

About