Title :
L1 tracker with spatially weighted similarity measure based clustering
Author :
Jianghua Dai ; Honghong Liao ; Weiping Sun ; Yu Shengsheng
Author_Institution :
Sch. of Comput. Sci. & Technol., Huazhong Univ. of Sci. & Technol., Wuhan, China
Abstract :
Recently, sparse representation has been successfully applied in visual tracking for its efficiency to varieties of corruptions. It is, however, unqualified for practical applications due to the extremely high computational expense of ℓ1 minimization. This paper proposes a new L1 tracker that resolves the above problem by clustering particles via k-means based on a spatially weighted similarity measure(SWSM) under particle filter framework. The SWSM which incorporates spatial relationships between particles into pixel-wise similarity measure is calculated for each particle pair, and then is fed for k-means clustering. After that, a two-stage selection based on ℓ2 and ℓ1 minimization respectively is applied to jointly determine the target state. Our L1 tracker keeps the diversity of particles from drifting and also largely promotes the tracking efficiency. The good performance of the proposed method is validated by comparison with two other state-of-the-art L1 tracker on four challenging sequences.
Keywords :
minimisation; object tracking; particle filtering (numerical methods); pattern clustering; ℓ1 minimization; ℓ2 minimization; L1 tracker; SWSM; k-means clustering; particle filter framework; particles clustering; pixel-wise similarity measure; sparse representation; spatial relationships; spatially weighted similarity measure based clustering; tracking efficiency; two-stage selection; visual tracking; Atmospheric measurements; Clustering algorithms; Computer vision; Minimization; Particle measurements; Target tracking; Visualization; Particle filter; Similarity measure; Sparse representation; Visual tracking;
Conference_Titel :
Mechatronic Sciences, Electric Engineering and Computer (MEC), Proceedings 2013 International Conference on
Conference_Location :
Shengyang
Print_ISBN :
978-1-4799-2564-3
DOI :
10.1109/MEC.2013.6885304