[1]SHAO Jiangnan,GE Hongwei.A long-term object tracking algorithm based on deep learning and object detection[J].CAAI Transactions on Intelligent Systems,2021,16(3):433-441.[doi:10.11992/tis.201910029]
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A long-term object tracking algorithm based on deep learning and object detection

CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume: 16 Number of periods: 2021 3 Page number: 433-441 Column: 学术论文—机器感知与模式识别 Public date: 2021-05-05
Title:
A long-term object tracking algorithm based on deep learning and object detection
Author(s):
SHAO Jiangnan1; 2;  GE Hongwei1; 2
1. Jiangsu Provincial Engineering Laboratory of Pattern Recognition and Computational Intelligence, Jiangnan University, Wuxi 214122, China;
2. School of Internet of Things, Jiangnan University, Wuxi 214122, China
Keywords:
object tracking; long-term tracking; neural network; convolutional features; class imbalance problem; loss function; feature extraction; deep learning
CLC:
TP391.4
DOI:
10.11992/tis.201910029
Abstract:
Aiming at the problem of tracking drift or loss caused by the occlusion and the out-of-view of the target in long-term tracking, this paper proposes a new deep, long-term tracking algorithm based on MDNet (LT-MDNet). First, an improved shrinkage loss function is introduced to solve the problem of the positive-negative class imbalance in the model training. Second, a high confidence retention sample pool is designed to retain the valid and highest confidence results of each frame during online tracking and to replace the lowest confidence retention samples when the pool is full. Finally, when the model detects a tracking failure or when the continuous tracking frame number reaches a specific threshold, the reserved sample pool is used for online training to update the model to make the model robust and efficient in dealing with long-term tracking. Experimental results show that LT-MDNet is highly competitive in its tracking accuracy and success rate and maintains excellent tracking performance and reliability in the case of target occlusion and out-of-view.
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