Combining Spatio-Temporal Context and Kalman Filtering for Visual Tracking

Haoran Yang, Juanjuan Wang, Yi Miao, Yulu Yang, Zengshun Zhao, Zhigang Wang, Qian Sun and Dapeng Oliver Wu
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Haoran Yang: College of Electronic and Information Engineering, Shandong University of Science and Technology, Qingdao 266590, China
Juanjuan Wang: College of Electronic and Information Engineering, Shandong University of Science and Technology, Qingdao 266590, China
Yi Miao: College of Electronic and Information Engineering, Shandong University of Science and Technology, Qingdao 266590, China
Yulu Yang: College of Electronic and Information Engineering, Shandong University of Science and Technology, Qingdao 266590, China
Zengshun Zhao: College of Electronic and Information Engineering, Shandong University of Science and Technology, Qingdao 266590, China
Zhigang Wang: Key Laboratory of Computer Vision and System, Ministry of Education, Tianjin Key Laboratory of Intelligence Computing and Novel Software Technology, Tianjin University of Technology, Tianjin 300384, China
Qian Sun: College of Electronic and Information Engineering, Shandong University of Science and Technology, Qingdao 266590, China
Dapeng Oliver Wu: Department of Electrical& Computer Engineering, University of Florida, Gainesville, FL 32611, USA

Mathematics, 2019, vol. 7, issue 11, 1-14

Abstract: As one of the core contents of intelligent monitoring, target tracking is the basis for video content analysis and processing. In visual tracking, due to occlusion, illumination changes, and pose and scale variation, handling such large appearance changes of the target object and the background over time remains the main challenge for robust target tracking. In this paper, we present a new robust algorithm (STC-KF) based on the spatio-temporal context and Kalman filtering. Our approach introduces a novel formulation to address the context information, which adopts the entire local information around the target, thereby preventing the remaining important context information related to the target from being lost by only using the rare key point information. The state of the object in the tracking process can be determined by the Euclidean distance of the image intensity in two consecutive frames. Then, the prediction value of the Kalman filter can be updated as the Kalman observation to the object position and marked on the next frame. The performance of the proposed STC-KF algorithm is evaluated and compared with the original STC algorithm. The experimental results using benchmark sequences imply that the proposed method outperforms the original STC algorithm under the conditions of heavy occlusion and large appearance changes.

Keywords: spatio-temporal context algorithm; Kalman filter; object detection; target tracking (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2019
References: View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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