Visual tracking using locality‐constrained linear coding under a particle filtering framework. Issue 2 (26th October 2017)

• Record Type: Journal Article
• Authors: Ding, Meng; Wei, Li; Cao, Yunfeng; Wang, Jie; Cao, Li
• Is Part Of: IET computer vision. Volume 12:Issue 2(2018)
• Page Start: 196
• Page End: 207
• Publication Date: 2017-10-26
• Subjects: linear codes -- particle filtering (numerical methods) -- target tracking; local region information -- computational complexity -- matrix calculation -- local feature descriptor -- LLC -- tracking algorithm -- spatiotemporal factors -- variable appearance -- visual target tracking -- particle filtering framework -- locality constrained linear coding -- visual tracking; Computer vision -- Periodicals; Pattern recognition systems -- Periodicals; 006.37
• DOI: 10.1049/iet-cvi.2017.0271
• Languages: English
• ISSNs: 1751-9632

Abstract

Abstract : Visual target tracking has long been a challenging problem because of the variable appearance of the target with changing spatiotemporal factors. Therefore, it is important to design an effective and efficient appearance model for tracking tasks. This study proposes a tracking algorithm based on locality‐constrained linear coding (LLC) under a particle filtering framework. A local feature descriptor is presented that can evenly represent the local information of each patch in the tracking region. LLC uses the locality constraints to project each local feature descriptor into its local‐coordinate system. Compared with sparse coding, LLC can be performed very quickly for appearance modelling because it has an analytical solution derived by a three‐step matrix calculation, and the computational complexity of the proposed tracking algorithm is o ( η × m × n ) . Both quantitative and qualitative experimental results demonstrate that the authors' proposed algorithm performs favourably against the 10 state‐of‐the‐art trackers on 12 challenging test sequences. However, related experimental results show that the performance of their tracker is not effective enough for small tracking targets owing to a lack of sufficient local region information.