Multi-Task Learning Tracking Method Based on the Similarity of Dynamic Samples

Algorithm 1: Proposed tracking method
Input: Initial target position v₁, pretrained local tracker.
Output: Estimated target position v_q
1: Initial training the local tracker by the ﬁrst frame, set number of consecutive failures P_cf=0.
2: for q =2:m
3: if P_cf≤r then locally draw candidate regions of target
4: else globally draw candidate regions of target
5: Get the tracking result and confidence score using local tracker
6: if (c_on > 0 and s_cos>H_cos and q>h) or (c_on>0 and q≤h) then
P_cf =0, collect new short-term and long-term samples
7: else P_cf=P_cf +1
8: if (q % b≠0) then update local tracker using Eq. (4)
9: if (q % b==0) then update local tracker using Eq. (6)

Table 2. Results of ablation experiments

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Table 2. Results of ablation experiments

Tracker	Detection of loss	Multi-task learning	Fast re-detection	Precision	Success rate
Baseline				0.9016	0.6677
Method 1	√			0.8986	0.6722
Method 2	√	√		0.9061	0.6788
Method 3	√		√	0.9032	0.6758
Method 4	√	√	√	0.9075	0.6812

Table 3. Tracking precision of 11 challengeable attributes

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Table 3. Tracking precision of 11 challengeable attributes

Tracker	IV	SV	OCC	DEF	MB	FM	IPR	OPR	OV	BC	LR
Proposed	0.909	0.889	0.858	0.876	0.854	0.877	0.907	0.898	0.838	0.937	0.937
MDNet	0.907	0.874	0.828	0.866	0.858	0.872	0.898	0.883	0.833	0.907	0.945
SiamRPN	0.859	0.838	0.780	0.826	0.816	0.789	0.854	0.851	0.725	0.799	0.978
SRDCF	0.792	0.745	0.734	0.735	0.765	0.768	0.745	0.741	0.593	0.775	0.760
SiamFC	0.736	0.736	0.723	0.691	0.707	0.744	0.743	0.758	0.672	0.692	0.900
Staple	0.783	0.726	0.728	0.752	0.698	0.708	0.768	0.737	0.664	0.749	0.690

Table 4. Testing result of the VOT-2016 dataset
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Table 4. Testing result of the VOT-2016 dataset
Tracker C-COT Staple MDNet SiamFC SRDCF Proposed
Accuracy 0.539 0.544 0.541 0.532 0.535 0.545
Robustness 0.238 0.378 0.337 0.461 0.419 0.317
AO 0.469 0.388 0.457 0.399 0.397 0.482

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Zaifeng Shi, Cheng Sun, Qingjie Cao, Zhe Wang, Qiangqiang Fan. Multi-Task Learning Tracking Method Based on the Similarity of Dynamic Samples[J]. Laser & Optoelectronics Progress, 2021, 58(16): 1615002

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