FAANet: feature-aligned attention network for real-time multiple object tracking in UAV videos

Fig. 1. Architecture of our tracker FAANet tracking framework. This framework contains four components: backbone (RepVGG), neck (CSA + FAA), head (Re-ID + detection), and association.

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Fig. 2. Architecture of CSA module.

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Fig. 3. Architecture of FAA module.

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Fig. 4. Procedure of association between detections and tracklets.

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Fig. 5. Structural re-parameterization of a RepVGG block.

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Fig. 6. MOTA-IDF1-FPS comparison with other UAV-based MOT trackers on the UAVDT test dataset. The horizontal axis is FPS, the vertical axis is MOTA, and the radius of the circle is IDF1.

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Fig. 7. IDF1 comparison with other UAV-based MOT trackers on the UAVDT test dataset based on scene attributes. The IDF1 of FAANet is marked outside the circle.

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Fig. 8. Examples and comparison of tracking results between DeepSORT and FAANet on the UAVDT test dataset.

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Table 1. Results of a Quantitative Comparison among Classic MOT Methods and Recent UAV-Based Methods on the UAVDT Test Dataset^a

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Table 1. Results of a Quantitative Comparison among Classic MOT Methods and Recent UAV-Based Methods on the UAVDT Test Dataset^a

MOT Methods	Year	Framework	MOTA $↑$	IDF1 $↑$	MOTP $↑$	MT $↑$	ML $↓$	FP $↓$	FN $↓$	IDS $↓$	FM $↓$	FPS $↑$
SORT^[1]	2016	Faster RCNN	39.0	43.7	74.3	33.9	28.0	33,037	172,628	2350	5787	Nan
DeepSORT^[2]	2017	Faster RCNN	40.7	58.2	73.2	41.7	23.7	44,868	155,290	2061	6432	15.01
DeepAlign^[20]	2018	Faster RCNN	41.6	49.0	73.3	43.7	24.3	45,420	152,224	1546	3733	0.23
SBMA^[21]	2019	LSTM	38.6	48.5	72.1	38.9	24.4	44,724	160,950	3489	11,796	Nan
IPGAT^[8]	2020	LSTM + CGAN	39.0	49.4	72.2	37.4	25.2	42,135	163,837	2091	10,057	Nan
M-CMSN-M^[9]	2020	Faster RCNN	43.1	62.6	73.5	45.3	22.7	45,900	147,638	390	4259	0.64
Quadruplet^[22]	2021	Faster RCNN	40.3	55.0	74.0	Nan	Nan	30,065	150,837	1091	3057	Nan
FAANet	Nan	RepVGG + JDE	44.0	64.6	77.9	47.9	22.6	57,146	133,496	403	7202	38.24

Table 2. Evaluation of the Critical Factors in FAANet^a
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Table 2. Evaluation of the Critical Factors in FAANet^a
RepVGG-B0 CA SA FAA MOTA $↑$ IDF1 $↑$ FPS $↑$
$\sqrt$ 38.2 56.8 45.70
$\sqrt$ $\sqrt$ 39.7 59.2 43.52
$\sqrt$ $\sqrt$ 39.3 59.4 43.41
$\sqrt$ $\sqrt$ $\sqrt$ 40.4 60.2 41.35
$\sqrt$ $\sqrt$ 42.1 63.7 40.54
$\sqrt$ $\sqrt$ $\sqrt$ $\sqrt$ 44.0 64.6 38.24

Table 3. The Improvement of Re-parameterization Technique
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Table 3. The Improvement of Re-parameterization Technique
Rep Params (10⁶) FLOPs (10⁹) MOTA $↑$ IDF1 $↑$ FPS $↑$
15.9 62.3 44.0 64.6 30.32
$\sqrt$ 14.4 58.3 44.0 64.6 38.24

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Zhenqi Liang, Jingshi Wang, Gang Xiao, Liu Zeng. FAANet: feature-aligned attention network for real-time multiple object tracking in UAV videos[J]. Chinese Optics Letters, 2022, 20(8): 081101

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