基于多相机信息协同的水面漂浮物跟踪方法研究

doi:10.12305/j.issn.1001-506X.2026.08.05

Abstract

Abstract:

Intelligent tracking technology of floating objects is crucial for water environment governance. However, existing methods are limited by complex background interference, imbalance between accuracy and efficiency, and single camera field of view limitations, resulting in insufficient detection accuracy and tracking performance. This study proposes a floating object tracking method based on multi-camera information collaboration, by integrating a lightweight backbone network and a dynamic feature pyramid network to balance detection accuracy and efficiency, introducing a fast directional gradient histogram and pyramid scale estimation strategy in the kernel correlation filtering algorithm, and proposing improved image matching algorithms for accurate tracking and matching; constructing a joint multi-camera spatial mechanism centered on the relevance of floating object monitoring coverage, the selection strategy of main tracking camera and auxiliary tracking camera, three camera tracking state selection schemes and cooperative tracking attitude relationship are proposed. The experimental results of the dataset of six types of floating objects show that the identity document F1(IDF1), identity document precision(IDP) and identity document recall(IDR) reach 89.16%, 94.52% and 92.01%, respectively, and the speed reaches 48.86 f/s, which effectively solves the difficulties of multi-scale floating object detection and cross-camera continuous tracking in complex environments.

Key words: deep learning, surface floater, multi-camera tracking, information collaboration

CLC Number:

Renfei CHEN, Jiahui CHEN, Zhongwen LI, Silin WANG. Water surface floating object tracking method based on multi-camera information collaboration[J]. Systems Engineering and Electronics, 2026, 48(8): 2545-2559.

Figures/Tables 18

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References 32

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视野区域	漂浮物范围	旋转方向
1	$ \boldsymbol{r }\in \left( {\left. {9\text {π} /8,11\text {π} /8} \right]} \right. $	左上
2	$ \boldsymbol{r }\in \left( {\left. {11\text {π} /8,13\text {π} /8} \right]} \right. $	上
3	$ \boldsymbol{r }\in \left( {\left. {13\text {π} /8,15\text {π} /8} \right]} \right. $	右上
4	$ \boldsymbol{r }\in \left( {\left. {7\text {π} /8,9\text {π} /8} \right]} \right. $	左
5	$ \left\| {\boldsymbol{r}} \right\| \lt R $	不变
6	$ \boldsymbol{r }\in \left[ {0,\text {π} /8} \right] \cup \left( {\left. {15\text {π} /8,2\text {π} } \right]} \right. $	右
7	$ \boldsymbol{r }\in \left( {\left. {7\text {π} /8,9\text {π} /8} \right]} \right. $	左下
8	$ \boldsymbol{r }\in \left( {\left. {3\text {π} /8,5\text {π} /8} \right]} \right. $	下
9	$ \boldsymbol{r }\in \left( {\left. {\text {π} /8,3\text {π} /8} \right]} \right. $	右下

场景	数量	分辨率	帧率/（f/s）
漂浮物尺度变化	15	1 920×1 080	30
水波干扰	15	1 920×1 080	30
光影变化	15	1 920×1 080	30
漂浮物平面外旋转	15	1 920×1 080	30

场景	类别/张	类别/张	实例/张	实例/张
漂浮物尺度变化	27 835	3.03	47 093	5.13
水波干扰	18 736	2.21	37 982	4.49
光影变化	13 092	2.06	28 739	4.52
漂浮物平面外旋转	9 837	1.76	20 913	3.74
总计	69 500	2.35	13 4727	4.55

类型	训练集（70%）		验证集（20%）		测试集（10%）
类型	图像/张	标签/个	图像/张	标签/个	图像/张	标签/个
水葫芦	6 836	34 180	1 953	9 766	976	4 883
塑料瓶	4 453	17 812	1 272	5 089	637	2 545
浮萍	3 333	16 665	952	4 761	477	2 381
金属罐	2 497	7 491	713	2 140	357	1 070
纸盒	2 293	9 172	655	2 621	328	1 310
塑料袋	2 087	10 435	596	2 981	298	1 491
总计	21 499	95 755	6 141	27 358	3 073	13 680

方法	评价指标	相机 15	相机16	相机17	相机22	相机23	相机24	相机25	总体
TRACTA^[27]	IDF1	78.28	81.45	81.23	79.34	80.19	83.65	77.91	80.29
	IDP	79.12	83.24	82.91	82.7	84.23	78.95	75.26	80.92
	IDR	75.15	79.32	78.42	75.84	80.27	74.92	73.36	76.75
TubeTK^[28]	IDF1	69.26	70.37	71.62	69.25	72.97	73.12	70.97	71.08
	IDP	70.13	73.94	72.33	72.82	75.67	74.25	72.77	73.13
	IDR	65.24	68.85	69.83	64.71	69.76	70.24	68.13	68.11
SA_ReID ^[29]	IDF1	70.43	69.92	72.31	73.93	66.25	68.16	60.26	68.75
	IDP	69.35	70.54	74.85	76.12	70.24	67.15	63.52	70.25
	IDR	65.61	63.54	68.86	67.27	62.97	63.63	58.24	64.30
LAAM ^[30]	IDF1	73.44	75.15	72.95	69.83	74.21	77.63	75.94	74.16
	IDP	74.93	77.72	73.81	67.24	73.85	80.26	79.14	75.28
	IDR	67.95	72.73	70.34	67.42	68.55	74.84	70.27	70.30
MCMT ^[31]	IDF1	84.38	87.39	89.27	88.25	86.16	88.29	85.13	86.98
	IDP	88.32	86.28	89.25	88.02	89.93	87.75	88.91	88.35
	IDR	81.93	82.37	86.79	83.28	86.82	88.93	84.24	84.91
BACTrack ^[32]	IDF1	81.03	82.38	84.17	83.19	81.11	84.03	80.23	82.31
	IDP	82.81	83.36	85.28	84.19	83.04	84.92	82.02	83.66
	IDR	78.92	80.17	80.28	81.22	81.17	80.16	78.91	80.12
MCMCT ^[11]	IDF1	86.11	86.29	88.19	87.29	88.28	88.37	87.06	87.37
	IDP	90.28	89.39	93.14	92.17	90.28	92.11	93.82	91.60
	IDR	88.21	89.93	90.06	89.36	89.94	88.25	90.91	89.52
本方法	IDF1	88.26	89.87	90.16	88.46	89.99	89.27	88.11	89.16
	IDP	94.38	93.17	95.45	94.33	93.19	95.93	95.19	94.52
	IDR	91.03	92.39	93.11	91.93	92.72	89.91	92.98	92.01

Water surface floating object tracking method based on multi-camera information collaboration

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实验	单相机参数初始化	多相机空间联合	ACLE	TP/%	FPS(f/s)
1	√	×	6.78	90.08	46.37
2	×	√	6.26	90.54	43.28
3	√	√	4.02	93.65	48.86

目标数量	评价指标	相机 15	相机16	相机17	相机22	相机23	相机24	相机25	总体
1	IDF1	88.26	89.87	90.16	88.46	89.99	89.27	88.11	89.16
	IDP	94.38	93.17	95.45	94.33	93.19	95.93	95.19	94.52
	IDR	91.03	92.39	93.11	91.93	92.72	89.91	92.98	92.01
2	IDF1	87.22	88.93	88.32	86.19	88.71	88.22	87.16	87.82
	IDP	93.91	91.93	94.02	92.91	92.88	93.81	94.03	93.36
	IDR	89.38	91.22	92.31	89.84	90.97	87.81	90.22	90.25
3	IDF1	85.02	85.18	86.02	83.39	86.27	85.29	85.61	85.25
	IDP	90.02	89.01	91.38	89.92	90.17	90.07	91.42	90.28
	IDR	87.39	89.22	90.28	86.92	88.39	85.31	88.31	87.97
4	IDF1	81.23	82.34	83.21	82.15	83.34	83.16	82.40	82.55
	IDP	84.24	86.15	87.21	86.86	88.33	87.26	83.19	86.18
	IDR	76.46	78.34	83.52	80.87	85.29	85.42	80.21	81.44
5	IDF1	79.21	80.43	81.65	81.74	80.53	81.22	80.32	80.73
	IDP	82.31	84.94	86.14	85.25	86.26	85.34	81.74	84.57
	IDR	74.82	77.24	81.82	78.43	83.98	83.76	78.47	79.79
6	IDF1	76.16	78.27	80.44	79.53	78.74	80.84	78.23	78.89
	IDP	79.32	81.43	83.34	83.24	84.21	84.11	79.47	82.16
	IDR	72.63	75.34	79.36	76.14	81.76	82.53	75.84	77.66

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