An Improved Discriminative Model Prediction Approach to Real-Time Tracking of Objects with Camera as Sensors

Document Type

Article

Publication Date

8-1-2021

Abstract

Generic person tracking is a basic task in visual surveillance by using camera as sensors. Many deep learning-based trackers have obtained outstanding performance. Among them, trackers based on Siamese networks have drawn great attention and are promising. These training methods are competitive but training data can be more effectively augmented to improve their person tracking performance. Many other trackers use only one layer to extract semantic features, likely hindering their discriminative learning. In this paper, we propose an enhanced discriminative model prediction method with efficient data augmentation and robust feature fusion. Specifically, we propose to implement an effective data augmentation strategy (e.g., color jitter and motion blur) to unleash the greater potential of original training data. We also adopt a multi-layer feature fusion to obtain a more discriminative feature map. Thus, the proposed tracker can discriminate an object in complicated scenarios in real time. We conduct extensive experiments on two datasets, i.e., VOT2018 and UAV123. Objective evaluation on VOT2018 demonstrates that with its expected average overlap value of 0.430, it outperforms a state-of-the-art tracker by 4.88%. On UAV123, it does so by 4.5% in success rate and 4.4% in precision rate. In addition, our further experimental results reveal that our algorithm can reach a speed that is high enough to meet the real-time tracking requirement when camera are used as sensors.

Identifier

85105855233 (Scopus)

Publication Title

IEEE Sensors Journal

External Full Text Location

https://doi.org/10.1109/JSEN.2021.3079214

e-ISSN

15581748

ISSN

1530437X

First Page

17308

Last Page

17317

Issue

15

Volume

21

Grant

61305014

Fund Ref

National Natural Science Foundation of China

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