目標跟蹤中基于IoU和中心點距離預測的尺度估計

李紹明; 儲珺; 冷璐; 涂序繼

doi:10.16383/j.aas.c210356

目標跟蹤中基于IoU和中心點距離預測的尺度估計

doi: 10.16383/j.aas.c210356

李紹明^{1, 2,},
儲珺^{1, 2,},
冷璐^{1, 2,},
涂序繼^{1, 2,}

1.
南昌航空大學軟件學院計算機視覺研究所南昌 330063
2.
江西省圖像處理與模式識別重點實驗室南昌 330063

基金項目: 國家自然科學基金(62162045)資助, 江西省科技支撐計劃項目20192BBE50073 資助

詳細信息

作者簡介:
李紹明：南昌航空大學軟件學院碩士研究生. 主要研究方向為計算機視覺, 目標跟蹤. E-mail: thorn_mo1905@163.com

儲珺：南昌航空大學軟件學院教授. 主要研究方向為計算機視覺, 模式識別. E-mail: chuj@nchu.edu.cn

冷璐：南昌航空大學軟件學院教授. 主要研究方向為圖像處理，生物特征模板保護和生物特征識別. E-mail: leng@nchu.edu.cn

涂序繼：南昌航空大學軟件學院講師. 主要研究方向為計算機視覺和圖像處理. E-mail: 71068@nchu.edu.cn

計量
- 文章訪問數: 641
- HTML全文瀏覽量: 394
- 被引次數: 0
出版歷程
- 收稿日期: 2021-04-24
- 錄用日期: 2021-11-02
- 網絡出版日期: 2021-11-29

Accurate Scale Estimation with IoU and Distance between Centroids for Object Tracking

LI Shao-Ming^{1, 2
,},
CHU Jun^{1, 2
,},
LENG Lu^{1, 2
,},
TU Xu-Ji^{1, 2
,}

1.
School of Software, Nanchang Hangkong University, Nanchang 330063
2.
Key Laboratory of Jiangxi Province for Image Processing and Pattern Recognition, Nanchang Hangkong University, Nanchang 330063

Funds: Supported by National Natural Science Foundation of P. R. China (62162045), supported by Jiangxi Provincial Science and Technology Key Project 20192BBE50073

More Information

Author Bio:
LI Shao-Ming　Master student at the School of Software, Nanchang Hangkong University. His research interests include computer vision and object tracking

Chu Jun　Professor at the School of Software, Nanchang Hangkong University. His research interest include computer vision and pattern recognition

LENG Lu　Professor at the School of Software, Nanchang Hangkong University. His research interests include image processing, biometric template protection and biometric recognition

TU Xu-Ji　Lecturer at the School of Software, Nanchang Hangkong University. His research interest include computer vision and image processing

摘要

摘要: 目標跟蹤中基于IoU (Intersection over union, IoU)預測的尺度估計方法, 通過估計視頻幀中候選框與真實目標框的重疊度訓練尺度回歸模型, 推理階段通過最大化IoU對初始化邊界框進行微調, 取得目標的尺度. 本文詳細分析了基于IoU預測的尺度估計模型的梯度更新過程, 發現其在訓練和推理過程僅將IoU作為度量, 缺乏對預測框和真實目標框中心點距離的約束, 導致外觀模型更新過程中模板受到污染, 前景和背景分類時定位出現偏差. 基于此發現, 本文構建了一種結合IoU和中心點距離的新度量NDIoU (Normalization distance IoU), 在此基礎上提出一種新的尺度估計方法, 并將其嵌入判別式跟蹤框架. 即在訓練階段以NDIoU為標簽, 設計了具有中心點距離約束的損失函數監督網絡的學習, 在線推理期間通過最大化NDIoU微調目標尺度, 以幫助外觀模型更新時獲得更加準確的樣本. 在七個數據上與相關主流方法進行對比, 本文方法在七個數據集上的綜合性能優于所有對比算法. 特別是在GOT-10k數據集上, 本文方法的AO、$ S{R}_{0.5} $和$ S{R}_{0.75} $三個指標達到了65.4%、78.7%和53.4%, 分別超過基線模型4.3%、7.0%和4.2%.
- 目標跟蹤 /
- IoU /
- 尺度估計 /
- 中心點距離
Abstract: The scale estimation based on IoU (Intersection over Union) prediction in object tracking trains the bounding box regression branch by estimating the IoU between the candidate box and the ground-truth in the video frame, and fine-tunes the initial bounding box by maximizing the IoU to obtain the object scale during inference. This paper first analyzes the gradient update process of the scale estimation model of IoU prediction in detail, and finds that when the IoU is used as a metric in the training and inference process, the target scale estimation in the tracking process is inaccurate due to the absence of distance between the two centroids. As a result, the template is polluted in the updating process of the object appearance model, which cannot discriminate the target and environment. With this insight, we propose a new metric NDIoU (Normalization Distance IoU) that combines the IoU and distance between two centroids to estimate the target scale and proposes a new scale estimation method, which is embedded into the discriminative tracking framework. NDIoU is used as the label to supervise the learning of the network and train the scale regression model. During online inference, NDIoU is maximized to fine-tune the target scale. Finally, the proposed method is embedded into the discriminative tracking framework and compared with related state-of-the-art methods on seven data sets. The extensive experiments demonstrate that our method outperforms all the state-of-the-art algorithms. Especially, on the GOT-10k data set, our method achieves 65.4%, 78.7% and 53.4% on the three metrics of AO, $ S{R}_{0.5} $ and $ S{R}_{0.75} $, which are better than the baseline by 4.3%, 7.0% and 4.2%, respectively.
- Object Tracking /
- IoU /
- Scale Estimation /
- Distance between Centroids
注釋:

1) 收稿日期?2021-04-24 錄用日期?2021-11-02 Manuscript?received?April?24,?2021;?accepted?November?2, 2021 國家自然科學基金 (62162045) 資助,?江西省科技支撐計劃項目20192BBE50073?資助 Supported?by?National?Natural?Science?Foundation?of?P.?R. China?(62162045),?supported?by?Jiangxi?Provincial?Science?and Technology?Key?Project?20192BBE50073 本文責任編委 Recommended?by?Associate?Editor 1.?南昌航空大學軟件學院計算機視覺研究所?南昌?330063 2.江西省圖像處理與模式識別重點實驗室?南昌?330063

2) 1.?School?of?Software,?Nanchang?Hangkong?University,?Nanchang?330063 2.?Key?Laboratory?of?Jiangxi?Province?for?Image Processing?and?Pattern?Recognition,?Nanchang?Hangkong?University,?Nanchang?330063

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圖 1 IoU相同, 但中心點距離不同的情況. 其中, 紅色代表候選的邊界框, 綠色代表真實邊界框

Fig. 1 Same IoU while different distances between centroids. Red represents the candidate bounding box, and green represents the ground-truth

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圖 2 標準化中心點之間的距離

Fig. 2 Normalized distance between centroids

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圖 3 IoU和中心點距離對應視頻幀數的統計

Fig. 3 The number statistics of video frame corresponding to IoU and distances between centroids

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圖 4 在視頻序列dinosaur上跟蹤的結果可視化

Fig. 4 Visualization of tracking results on the video sequence dinosaur

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圖 5 本文方法(ASEID)在OTB-100上與相關方法的比較

Fig. 5 Comparison of the proposed method (ASEID) with related algorithms on OTB-100

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圖 6 OTB-100 數據集不同挑戰性因素影響下的成功率圖

Fig. 6 Success plots on sequences with different challenging attributes on OTB-100 dataset

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圖 7 OTB-100 數據集不同挑戰性因素影響下的準確率圖

Fig. 7 Precision plots on sequences with different challenging attributes on OTB-100 dataset

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圖 8 本文方法與相關方法的可視化比較

Fig. 8 Visualization comparison of the proposed method and related trackers

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圖 9 OTB-100數據集中的失敗案例. 綠色框代表真實框, 紅色框代表本文算法的預測框.

Fig. 9 Failure cases in OTB-100. The green bounding box is ground truth, and the red box represents the prediction box.

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圖 10 GOT-10k數據集中的失敗案例. 在GOT-10k的測試集中, 由于只能拿到測試視頻序列的第一幀的真實框, 因此第一幀的標記代表被跟蹤目標.

Fig. 10 Failure cases in GOT-10k. In GOT-10k test set, only the ground truth in the first frame of the test video sequence can be obtained. Therefore, the ground truth of the first frame represents the tracked target.

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表 1 OTB-100上的消融實驗

Table 1 Ablation study on OTB-100

	AUC (%)	Precision (%)	Norm.Pre (%)	FPS
多尺度搜索	68.4	88.8	83.8	21
IoU	68.4	89.4	84.2	35
NDIoU	69.8	91.3	87.3	35

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表 2 在UAV123上和SOTA算法的比較

Table 2 Compare with SOTA trackers on UAV123

	SiamBAN^[33]	CGACD^[34]	POST^[35]	MetaRTT^[36]	ECO^[28]	UPDT^[32]	DaSiamRPN^[37]	ATOM^[7]	DiMP50 (baseline)^[14]	ASEID (ours)
AUC (%)	63.1	63.3	62.9	56.9	52.4	54.2	56.9	63.2	64.3	64.5
Precision (%)	83.3	83.3	80.0	80.9	74.1	76.8	78.1	84.4	85.0	86.1
Norm.Pre (%)	—	—	—	—	66.8	70.9	74.2	79.1	80.5	81.6

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表 3 在VOT2018上與SOTA方法的比較

Table 3 Compare with SOTA trackers on VOT2018

	DRT^[38]	RCO^[39]	UPDT^[32]	DaSiamRPN^[37]	MFT^[39]	LADCF^[40]	ATOM^[9]	SiamRPN++^[16]	Dimp50 (baseline)^[14]	PrDiMP50^[15]	ASEID (ours)
EAO	0.356	0.376	0.378	0.383	0.385	0.389	0.401	0.414	0.440	0.442	0.454
Robustness	0.201	0.155	0.184	0.276	0.140	0.159	0.204	0.234	0.153	0.165	0.153
Accuracy	0.519	0.507	0.536	0.586	0.505	0.503	0.590	0.600	0.597	0.618	0.615

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表 4 在GOT-10k上與SOTA方法的比較

Table 4 Compare with SOTA trackers on GOT-10k

	DCFST^[30]	PrDiMP50^[15]	KYS^[17]	SiamFC++^[13]	D3S^[41]	Ocean^[12]	ROAM^[31]	ATOM^[7]	DiMP50 (baseline)^[14]	ASEID (ours)
$ \mathit{S}{\mathit{R}}_{0.50}\left (\mathbf{\%}\right) $	68.3	73.8	75.1	69.5	67.6	72.1	46.6	63.4	71.7	78.7
$ \mathit{S}{\mathit{R}}_{0.75} $ (%)	44.8	54.3	51.5	47.9	46.2	—	16.4	40.2	49.2	53.4
$ \mathit{A}\mathit{O}\left (\mathbf{\%}\right) $	59.2	63.4	63.6	59.5	59.7	61.1	43.6	55.6	61.1	65.4

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表 5 在LaSOT上與SOTA方法的比較

Table 5 Compare with SOTA trackers on LaSOT

	ASRCF^[6]	POST^[35]	Ocean^[12]	GlobalT^[42]	SiamRPN++^[19]	ROAM^[31]	ATOM^[9]	DiMP50 (baseline)^[14]	ASEID (ours)
Precision (%)	33.7	46.3	56.6	52.7	56.9	44.5	50.5	56.9	57.5
Success (AUC) (%)	35.9	48.1	56.0	52.1	49.6	44.7	51.4	56.9	57.2

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表 6 在TrackingNet上與SOTA方法的比較

Table 6 Compare with SOTA trackers on TrackingNet

	MDNet^[29]	ECO^[28]	DaSiamRPN^[37]	D3S^[41]	ROAM^[31]	CGACD^[34]	ATOM^[9]	Dimp50 (baseline)^[14]	ASEID (ours)
AUC (%)	60.6	55.4	63.8	72.8	67.0	71.1	70.3	74.0	75.3
Precision (%)	56.5	49.2	59.1	66.4	62.3	69.3	64.8	68.7	71.1
Norm.Pre (%)	70.5	61.8	73.3	—	—	—	77.1	80.1	81.9

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表 7 在TC128上與SOTA算法比較

Table 7 Compare with SOTA trackers on TC128

	POST^[35]	MetaRTT^[36]	ASRCF^[6]	UDT^[43]	TADT^[44]	Re2EMA^[45]	RTMDNet^[46]	MLT^[47]	DiMP50 (baseline)^[14]	ASEID (ours)
AUC (%)	56.3	59.7	60.3	54.1	56.2	52.1	56.3	49.8	61.2	63.2
Precision (%)	78.1	80.0	82.5	71.7	—	69.5	78.8	—	81.0	84.2

下載: 導出CSV

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